Education — 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 the other topics.
Growth
61 measurements spanning 91 days, net +459. 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”.
Measurement log — every subscribers count we have recorded, most recent 20 of 61
Measured (UTC)
Subscribers
Change
6 Nov 2026, 06:19
40,200
+3
5 Nov 2026, 06:38
40,197
-12
4 Nov 2026, 04:50
40,209
-13
3 Nov 2026, 02:59
40,222
+40,196
2 Nov 2026, 10:15
26
-40,199
2 Nov 2026, 04:20
40,225
-18
1 Nov 2026, 05:56
40,243
-12
31 Oct 2026, 08:55
40,255
-23
30 Oct 2026, 09:36
40,278
+11
29 Oct 2026, 09:00
40,267
-1
28 Oct 2026, 10:15
40,268
+7
27 Oct 2026, 09:45
40,261
+1
24 Oct 2026, 12:39
40,260
-5
23 Oct 2026, 13:46
40,265
+6
22 Oct 2026, 11:00
40,259
+112
21 Oct 2026, 06:59
40,147
+40,125
20 Oct 2026, 03:47
22
-40,112
20 Oct 2026, 03:11
40,134
-11
18 Oct 2026, 01:49
40,145
-1
17 Oct 2026, 00:56
40,146
first reading
Engagement
49 posts held, back to 2 August 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 171 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
7.88%
avg views ÷ 40,200 subscribers
Avg views / post
3,170
15 posts measured
Reaction rate
1.55%
reactions ÷ views · ER floor
Posts in window
15
of 49 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 4 November 2026
Posts held
49 (2 August 2026 – 4 November 2026)
Views total
47,530
Reactions total
737
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
6 Nov 2026, 15:13 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
≈3,930
Videos
≈123
Links
≈2,030
Lifetime counters from Telegram’s own channel header, read 6 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Reaction mix
2,034 reactions across 48 posts, in 10 distinct kinds. The most used accounts for 48.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
995
48.9%
❤🔥
428
21.0%
🔥
374
18.4%
😱
90
4.42%
👏
66
3.24%
⚡
28
1.38%
🥰
21
1.03%
🤯
20
0.983%
👍
11
0.541%
🤔
1
0.049%
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 48 of the 49 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 2,034 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 49 most recent posts we hold, published 2 August 2026 to 4 November 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.
Recent posts
Telegram’s own ‘edited’ marker: 1 of 15 posts held from the last 30 days (6.67%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
Уолту Диснею и не снилось. ❓
Сегодня на прием пришел 12-летний мальчик с необычной сыпью на обеих голенях.
📍 Anamnesis vitae: Высыпаний раньше не было. Прием любых лекарств пациент отрицает. Недавние инфекции — тоже. С представителями фауны и флоры он также не контактировал.
📍 Anamnesis morbi: Пациент чувствует себя хорошо, температуры нет. Ничего не болит: ни суставы, ни живот. Других жалоб тоже нет.
Клинический…
«Аллергология и иммунология» на стероидах.
С высокой концентрацией практической медицины и широким спектром показаний — от крапивницы до иммунодефицитов.
📌 Состав: разборы реальных случаев, гайдлайны и клинический опыт спикеров, которые знают специальность изнутри.
📌 Схема приема: живые семинары в формате «перевернутого класса», где вместо повторения теории — её применение.
📌 Взаимодействия: в закрытом чате со спик…
Кошачья царапина, легионер или криоглобулин?
🎃 А какой костюм на Хэллоуин выбрали бы вы?
Наша команда уже определилась — и даже устроила небольшой медицинский показ мод. Встречайте специальный хэллоуинский выпуск карточек!
Сможете угадать все образы? Ждем ваши версии в комментариях 🧡
Когда мумии продавались в аптеках. 🐈⬛
И почему это вряд ли бы произошло, будь у средневековых европейцев доступ к Google переводчику.
На протяжении нескольких сотен лет, особенно в XVI и XVII веках, по всему континенту пользовались популярностью лекарства, содержащие человеческие кости, кровь и жир.
🌟Римляне пили кровь убитых гладиаторов, которой приписывали способность излечить эпилепсию.
🌟 Король Англии Карл II…
Ужасно хорошие новости. 😮
На прошлой неделе невероятная Юлия, выпускница нашего курса подготовки к USMLE Step 1, получила тот самый PASS.
Ещё не успели поздравить её ❤️ под этим постом? Тогда скорее сделайте это прямо сейчас! А Юлия пока поделится своими эмоциями с нами:
Ребята, всем привет! 💛
Я окончила Челябинскую государственную медицинскую академию в 2010 году, а в декабре 2019 года переехала в США. В 2020 г…
Эпидемия взрывающихся зубов.
В XIX веке стоматолог Уильям Аткинсон столкнулся с загадочным явлением, которое больше напоминало сюжет кошмара. Всё начиналось с невыносимой зубной боли.
🦷 Правый верхний клык начал болеть до такой степени, что пациент впал в ярость. Он метался туда-сюда, пытаясь обрести хоть какое-то облегчение: то он начал биться головой о землю, то пошел к источнику и погрузил голову до самого дна в…
Слышу звон, да не знаю, где он.
🔔 Буквально с такой жалобой столкнулся ЛОР-врач — и по совместительству читатель нашего канала.
Приходит ко мне на приём пациент. Взволнованный, невыспавшийся. С порога говорит:
💬 «Доктор, у меня что-то стучит в голове. Особенно когда резко её поворачиваю или наклоняюсь. Ночью просыпаюсь от ощущения, что там что-то перекатывается. Я уже записался к психиатру, думал, что схожу с ума»…
Если мир держится на черепахе, то медицина — на анатомии. 🐢
В качестве подтверждения представляем сегодняшнюю порцию карточек, включающую пять различных клинических сценариев из травматологии:
❓ Почему у гольфиста болит локоть, а у велосипедиста немеют пальцы?
❓ Из чего состоит «несчастливая триада» и кто расстроил колено?
❕ Внимание: для их создания нам не потребовались учебники по неврологии или травматологии — …
У врачей есть два режима обучения:
🥲 «Бей-или-беги». Три гайдлайна, лекция и клинический случай — когда открыто всё, но ничего не складывается в одно.
❤️ «Отдыхай-и-переваривай».
Его включаем в «Высшей Школе Терапии» уже в эту пятницу!
Впереди — год терапии, где клинические случаи наконец перестают распадаться на отдельные вкладки.
✔️ Живые разборы реальных кейсов с опытными врачами вместо очередного пересказа ре…
Фабрика гормонов: как работает щитовидная железа? 🏭
Казалось бы, что может быть проще: назначил анализы, посмотрел на стрелочки «выше / ниже нормы» — и диагноз готов. Но на практике именно сочетание показателей вызывает больше всего вопросов.
Как же расшифровать гормоны щитовидной железы? В новых карточках учимся переводить показатели на язык клинициста.
И как в объеме до 25 мл умещается эта система регуляции мета…
Мона Лиза: история болезни.
Официально она началась в 2004 году, когда группа ревматологов и эндокринологов заметила на портрете не просто мастерскую работу кистью, но и возможный клинический признак — желтоватое образование у внутреннего угла левого века. 🔎
🦪 Ксантелазма?
Эта находка навела авторов на мысль о гиперлипидемии. А учитывая возраст Моны Лизы, они упомянули её наследственную форму — семейную гиперхолес…
Пять веков без диагноза. 🦪
Мона Лиза веками не дает покоя искусствоведам. Но не только им: врачи тоже пытались разгадать загадку женщины с самой известной улыбкой в мире.
Поставить диагноз по картинке может каждый. А как насчет диагноза по картине?
Делитесь своими идеями в комментариях. Мы ждем любые версии: от очевидных до самых неожиданных. 🤩
P.S. Аргументы приветствуются, клиническая фантазия — тоже.
❤19🔥5😱4👍2
Showing the 12 most recent of 49 posts we hold for @synapse_channel_1. 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.
Posts recorded as deleted after publishing
We recorded 6 posts on @synapse_channel_1 as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
1 September 2026
Most recent seen missing
22 October 2026
Median time before seen missing
2 days
Seen missing in the last 30 days
3
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 4 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 4 September 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.
MD.school | Медицинский онлайн-университет | Доказательная медицина @medicina_discendi · 21,254 Telegram ranks this channel #7 of 98 here — alongside 97 others — read 25 September 2026
Медач | Medical Channel @medach · 27,957 Telegram ranks this channel #10 of 97 here — alongside 96 others — read 10 September 2026
Библиотека Умного Медика @smart_medics · 20,232 Telegram ranks this channel #12 of 87 here — alongside 86 others — read 27 September 2026
Медицинский Блог | Медицина @medproblog · 29,455 Telegram ranks this channel #18 of 82 here — alongside 81 others — read 8 September 2026
Медицинский блог Сеченова @medblognomer1 · 24,211 Telegram ranks this channel #31 of 77 here — alongside 76 others — read 16 September 2026
Pixorize Videos | USMLE Step 1 @USMLEPixorize · 26,521 Telegram ranks this channel #37 of 82 here — alongside 81 others — read 13 September 2026
Ordinatura.org | Ординатура орг @ordinatura_org · 24,186 Telegram ranks this channel #47 of 90 here — alongside 89 others — read 16 September 2026
Физиология человека @fiziologii1tj · 28,350 Telegram ranks this channel #48 of 86 here — alongside 85 others — read 16 September 2026
Юрист для врача Ангелина Романовская @mfcexpert_news · 29,885 Telegram ranks this channel #50 of 96 here — alongside 95 others — read 8 September 2026
Анестезиология-реаниматология. Ординаторы Asia+ @orda_air · 20,604 Telegram ranks this channel #55 of 88 here — alongside 87 others — read 1 October 2026
Медицинская Литература @clinirec · 36,079 Telegram ranks this channel #55 of 84 here — alongside 83 others — read 4 September 2026
Medbooks @supermedbook · 20,858 Telegram ranks this channel #60 of 69 here — alongside 68 others — read 28 September 2026
Bilgi Bilek Büker TUS Paylaşım Grubu @bbbdahiliye · 24,177 Telegram ranks this channel #61 of 64 here — alongside 63 others — read 20 September 2026
Аккредитация врачей | Ординатура @credit_med · 23,165 Telegram ranks this channel #64 of 96 here — alongside 95 others — read 19 September 2026
Профессия – кардиолог @profcardiologist · 20,931 Telegram ranks this channel #66 of 95 here — alongside 94 others — read 26 September 2026
Reclin - live @reclinlive · 31,479 Telegram ranks this channel #67 of 88 here — alongside 87 others — read 7 September 2026
Справочник педиатра @pediatrhelp · 19,282 Telegram ranks this channel #81 of 92 here — alongside 91 others — read 2 October 2026
Physeo Videos Step 1 | CK @USMLEPhyseo · 32,439 Telegram ranks this channel #83 of 84 here — alongside 83 others — read 5 September 2026
Поясни за мед @poyasni_za_med · 22,218 Telegram ranks this channel #95 of 97 here — alongside 96 others — read 21 September 2026
This channel appears in 19 seed channels' Telegram-generated recommendation lists in total. 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 6 November 2026 — this
entry's latest reading, not the date you are reading this.
“SYNAPSE I СИНАПС” (@synapse_channel_1), 40,200 subscribers as measured 6 November 2026. Telegram Register, tgregister.com/channel/synapse_channel_1.
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.