Health & wellness — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 9 September 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
44 measurements spanning 78 days, net +46. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 39,292–39,458 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 44
Measured (UTC)
Subscribers
Change
22 Oct 2026, 14:45
39,385
-10
21 Oct 2026, 07:16
39,395
-3
20 Oct 2026, 04:57
39,398
-1
19 Oct 2026, 01:17
39,399
-10
17 Oct 2026, 00:26
39,409
-14
16 Oct 2026, 02:48
39,423
-6
15 Oct 2026, 05:28
39,429
+4
14 Oct 2026, 03:50
39,425
-3
11 Oct 2026, 20:40
39,428
-11
7 Oct 2026, 09:40
39,439
+24
2 Oct 2026, 13:58
39,415
+27
18 Sept 2026, 13:38
39,388
-6
16 Sept 2026, 04:00
39,394
-8
14 Sept 2026, 08:38
39,402
-9
12 Sept 2026, 18:41
39,411
-8
10 Sept 2026, 22:16
39,419
+21
8 Sept 2026, 00:18
39,398
+1
5 Sept 2026, 00:59
39,397
+4
3 Sept 2026, 09:03
39,393
-7
2 Sept 2026, 05:52
39,400
first reading
Engagement
64 posts held, back to 5 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 145 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
6.18%
avg views ÷ 39,385 subscribers
Avg views / post
2,430
39 posts measured
Reaction rate
3.88%
reactions ÷ views · ER floor
Posts in window
41
of 64 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 22 October 2026
Posts held
64 (5 July 2026 – 22 October 2026)
Views total
94,870
Reactions total
3,680
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
23 Oct 2026, 06:46 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Photos
803
Videos
243
Links
672
Lifetime counters from Telegram’s own channel header, read 23 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Video runtime
1h 09m
Average length
17m 18s
Measured directly from 4 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
7,350 reactions across 60 posts, in 18 distinct kinds. The most used accounts for 51.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
3,804
51.8%
❤🔥
917
12.5%
🔥
901
12.3%
😢
818
11.1%
🙏
314
4.27%
👍
240
3.27%
👏
93
1.27%
😱
75
1.02%
🥰
66
0.898%
🕊
35
0.476%
🫡
26
0.354%
🤔
16
0.218%
💯
15
0.204%
🤗
13
0.177%
🤩
8
0.109%
👌
6
0.082%
🤯
2
0.027%
🎉
1
0.014%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 62 of the 64 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 7,350 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 64 most recent posts we hold, published 5 July 2026 to 22 October 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
59
across the posts below
Posts paid on
26
of 62 we hold a reading for · 42%
Most on one post
11
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @allergo_doc. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 64 most recent posts we hold for this entry, published 5 July 2026 to 22 October 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Advertising
Ad load
1.56%
1 of 64 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
1,830
over 1 measured post
Median views · rest
3,600
over 61 measured posts
An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.
This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.
Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.
Advertising tokens recorded on this entry
erid
Posts
First seen
Last seen
CQH36pWzJq6L1R5aFiJb9fqCNsS22aYPUkidT6cjnqFbKw
1
20 October 2026
20 October 2026
A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.
Measured over the 64 most recent posts we hold, published 5 July 2026 to 22 October 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.
Написала этот текст некоторое время назад, поэтому кто-то его видел в инсте. Для меня он по-прежнему актуален.
Кто-то запускает и закрывает бизнес постоянно, с легкостью. Для меня клиника никогда не была «просто бизнесом». Это было воплощение идеи о классном месте для пациентов и сотрудников. Безопасном, комфортном, экспертном. У меня не было инвесторов, не было поддержки, кроме психологической (но это очень много, …
Тем временем 1MedTV запросил у меня возможность записать лекцию по итогам WAO (а точнее тех секций, которые я посетила, будем честны). Параллельно начала готовить переводы своих лекций для врачей на английский. Очень старательно стараюсь, challenge accepted, как говорится. Волнительно, но с другой стороны, это рост.
Наконец-то мое сражение с выкладыванием видео увенчалось успехом и все, кто терпеливо ждал запись вебинара, наконец-то вознаграждены.
Прошел уже почти месяц с закрытия клиники, у меня по этому поводу много мыслей и чувств, но я не уверена, насколько все это заслуживает обсуждения здесь. Лично у меня подобные тексты в профессиональных блогах вызывают спорные ощущения, но я хочу спросить у вас: вам это интересно?
Advertisementerid CQH36pWzJq6L1R5aFiJb9fqCNsS22aYPUkidT6cjnqFbKwForwarded from @antropogenez_ruPhoto
Научно-просветительский форум «Ученые против мифов. Имеются противопоказания»
Москва, Сколтех | 28 ноября (сб.)
Внимание: форум избавляет от псевдонаучных заблуждений в области медицины, а также снижает риск когнитивных искажений. Активные компоненты форума предохранят ваш мозг от конспирологических поражений и предотвратят дистрофию кошелька при посещении аптек и медицинских учреждений.
Имеются противопоказания! И…
Mário Morais-Almeida (Allergy Center, CUF-Descobertas Hospital, Portugal)
Фенотипы астмы у детей
🔑 Коротко: фенотип - это снимок, а астма - это фильм. Астма меняется с течением времени вместе с ростом и развитием ребёнка, созреванием иммунной системы и тем лечением, которое мы проводим. Поэтому главный вопрос не «какой у ребёнка фенотип?», а «куда он движется?».
📊 Фенотипы, которые важны для прогноза
- Множественная …
Gary W. K. Wong (The Chinese University of Hong Kong) Профилактика астмы: мечта или реальность?
👨⚕️ Кто это: профессор педиатрии, один из ведущих исследователей различий в распространённости астмы между городскими и сельскими детьми в Китае.
🔑 Коротко: до 1950-х годов астма была относительно редкой болезнью. Её быстрый рост во второй половине XX века объясняется изменениями среды, а не генетики. Самая устойчивая нах…
💡 Что это значит на практике: самое доказанное и доступное — защитить ребёнка от табачного дыма (и вейпов) ещё до рождения, всей семьёй, и использовать профилактику РСВ-инфекции там, где она доступна. Остальное разумно как часть здорового образа жизни, но не как гарантия профилактики астмы.
Kallis C, et al. Thorax. 2023
Xepapadaki et al. Pediatr Allergy Immunol. 2009
Turner S, et al. Lancet Public Health. 2020;5(9):e…
Nikolaos G. Papadopoulos (University of Athens) Профилактика свистящего дыхания (бронхиальных обструкций) у детей: что мы можем изменить
🔑 Коротко: свистящее дыхание в раннем детстве встречается часто и у части детей становится первым шагом к астме. Одного единого способа его предотвратить нет, но часть факторов риска можно изменить, и по некоторым из них доказательства сильные, а мы до сих пор плохо применяем их на …
Shimon Sakaguchi (The University of Osaka) Регуляторные Т-клетки: можно ли «переучить» иммунитет при аллергии
👨⚕️ Кто это: первооткрыватель регуляторных Т-клеток, лауреат Нобелевской премии по физиологии и медицине 2025 года.
🔑 Коротко: регуляторные Т-клетки работают как «тормоз» иммунной системы. Они не дают ей атаковать собственные ткани, а также безвредные вещества, например пищу. Когда «тормоз» не срабатывает, р…
Motohiro Ebisawa (Sagamihara National Hospital) Низкодозовая оральная иммунотерапия: японский опыт
👨⚕️ Кто это: один из ведущих мировых экспертов по пищевой аллергии, руководитель группы, разработавшей японскую модель ступенчатых провокационных проб.
🔑 Коротко: в Японии широко используют низкодозную схему ОИТ. Цель скромнее: не «есть продукт свободно», а безопасно переносить небольшое количество и не реагировать на …
❤56🙏5
Showing the 12 most recent of 64 posts we hold for @allergo_doc. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@allergo_doc edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
18 October 2026
Most recent edit
18 October 2026
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
The shares total 126%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 64 most recent posts we hold, published 5 July 2026 to 22 October 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 7 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 31 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Шура, выручай! @shura_help · 34,467 Telegram ranks this channel #7 of 95 here — alongside 94 others — read 3 September 2026
Doctor_marusenyka @sovetdaymotiv · 23,860 Telegram ranks this channel #9 of 97 here — alongside 96 others — read 17 September 2026
doctor_annamama @doctor_annamama · 82,617 Telegram ranks this channel #9 of 90 here — alongside 89 others — read 18 August 2026
Смотровая Военврача @dr_voenvrach · 44,361 Telegram ranks this channel #11 of 98 here — alongside 97 others — read 28 August 2026
Заметки детского врача @DrButriy · 137,692 Telegram ranks this channel #20 of 95 here — alongside 94 others — read 13 August 2026
Эффект Бочковой @OlgaBochkovaAcademy · 19,904 Telegram ranks this channel #21 of 92 here — alongside 91 others — read 29 September 2026
ImmunoBee @immunobee1 · 104,230 Telegram ranks this channel #21 of 95 here — alongside 94 others — read 16 August 2026
Гастроэнтеролог ХАРИТОНОВ Андрей Геннадьевич @ag_kharitonov · 58,871 Telegram ranks this channel #26 of 99 here — alongside 98 others — read 22 August 2026
ninavaccina @ninavaccina · 90,238 Telegram ranks this channel #27 of 96 here — alongside 95 others — read 17 August 2026
Кардиолог Новикова Диана Сергеевна @cardiolognovikova · 24,459 Telegram ranks this channel #28 of 96 here — alongside 95 others — read 15 September 2026
Доктор Неешпапа 🫁 /doctor_neeshpapa/ @doctor_neeshpapa · 31,901 Telegram ranks this channel #28 of 96 here — alongside 95 others — read 5 September 2026
Занимательная эндокринология @funwithmedicine · 22,735 Telegram ranks this channel #33 of 98 here — alongside 97 others — read 20 September 2026
Юлия Сидорова |Детский эндокринолог| @sidorovasay · 26,662 Telegram ranks this channel #36 of 93 here — alongside 92 others — read 11 September 2026
Юрист для врача Ангелина Романовская @mfcexpert_news · 29,817 Telegram ranks this channel #37 of 96 here — alongside 95 others — read 8 September 2026
Федиатрия @fediatrix · 54,022 Telegram ranks this channel #37 of 96 here — alongside 95 others — read 24 August 2026
MAMA_SINKA @mama_sinka · 40,825 Telegram ranks this channel #44 of 96 here — alongside 95 others — read 29 August 2026
Занудная педиатрия — Роман Шиян @drshnr · 37,073 Telegram ranks this channel #45 of 94 here — alongside 93 others — read 1 September 2026
Намочи манту @namochimanturu · 56,296 Telegram ranks this channel #45 of 95 here — alongside 94 others — read 23 August 2026
Пей таблетки! | MedIQLab @mediq · 65,727 Telegram ranks this channel #45 of 93 here — alongside 92 others — read 21 August 2026
Помощник педиатра | МедикУм @pomoshnik_pediatra · 23,794 Telegram ranks this channel #47 of 94 here — alongside 93 others — read 17 September 2026
Доктор Молодой @doctor_dmolodoy · 61,697 Telegram ranks this channel #54 of 94 here — alongside 93 others — read 22 August 2026
Гастроэнтеролог Саша @gastrosasha95 · 40,948 Telegram ranks this channel #58 of 94 here — alongside 93 others — read 30 August 2026
Нефролог I Данила Пыриков @pyrikov · 67,219 Telegram ranks this channel #58 of 98 here — alongside 97 others — read 20 August 2026
MarysStories @Marys_stories · 31,413 Telegram ranks this channel #59 of 92 here — alongside 91 others — read 5 September 2026
This channel appears in 37 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 22 October 2026 — this
entry's latest reading, not the date you are reading this.
“Аллергология для чайников с Ольгой Жоголевой” (@allergo_doc), 39,385 subscribers as measured 22 October 2026. Telegram Register, tgregister.com/channel/allergo_doc.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.