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Created
1 April 2020 — measured — cross-checked against a third-party dataset (ext.tg_channel)
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 10 September 2026 and assigned it the closest of 31 fixed categories, at 93% 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.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
33 measurements spanning 42 days, net -510. 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 27,866–28,529 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
17 Sept 2026, 16:17
27,942
-20
15 Sept 2026, 14:41
27,962
-27
14 Sept 2026, 01:01
27,989
-14
12 Sept 2026, 08:59
28,003
-31
10 Sept 2026, 05:15
28,034
-39
6 Sept 2026, 21:38
28,073
-33
4 Sept 2026, 09:35
28,106
-18
2 Sept 2026, 19:08
28,124
-8
1 Sept 2026, 20:28
28,132
-10
31 Aug 2026, 20:46
28,142
-24
30 Aug 2026, 20:56
28,166
-3
30 Aug 2026, 00:19
28,169
-11
28 Aug 2026, 23:34
28,180
-21
27 Aug 2026, 22:24
28,201
-13
27 Aug 2026, 01:03
28,214
-11
25 Aug 2026, 21:27
28,225
-11
25 Aug 2026, 00:34
28,236
-24
23 Aug 2026, 14:58
28,260
-16
21 Aug 2026, 22:06
28,276
-11
20 Aug 2026, 16:56
28,287
first reading
Engagement
99 posts held, back to 8 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 66 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
9.15%
avg views ÷ 27,942 subscribers
Avg views / post
2,560
40 posts measured
Reaction rate
1.17%
reactions ÷ views · ER floor
Posts in window
40
of 99 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 23 September 2026
Posts held
99 (8 July 2026 – 23 September 2026)
Views total
102,320
Reactions total
1,193
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
24 Sept 2026, 18:12 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
≈4,000
Videos
≈594
Links
≈4,900
Lifetime counters from Telegram’s own channel header, read 24 September 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.
Video runtime
33m 01s
Average length
2m 22s
Measured directly from 14 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
2,856 reactions across 97 posts, in 29 distinct kinds. The most used accounts for 26.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
745
26.1%
🙏
417
14.6%
👍
299
10.5%
👏
262
9.17%
🔥
252
8.82%
💯
206
7.21%
❤🔥
170
5.95%
🤬
93
3.26%
🤡
84
2.94%
🤮
54
1.89%
🤣
44
1.54%
🤔
35
1.23%
👌
31
1.09%
😱
27
0.945%
😭
22
0.77%
🏆
21
0.735%
🤯
20
0.7%
👎
15
0.525%
🤩
14
0.49%
😁
10
0.35%
9 further kinds
35
1.23%
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 97 of the 99 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 2,856 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 99 most recent posts we hold, published 8 July 2026 to 23 September 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.
McGill’s Office for Science and Society wrote the story the world tells about Sayer Ji. Here is the audit it never ran on itself.
https://sayerji.substack.com/p/why-did-mcgill-target-me
On X https://x.com/sayerjigmi/status/2102609265764745610?s=20
🇺🇸 On Constitution Day, we officially launched Restore the First in Washington, D.C.
A constitutional right must come with a meaningful way to defend it.
Join our growing national coalition to demand accountability for government censorship and enforceable protections for every American—whatever your politics.
Take three concrete actions:
1️⃣ Email your lawmakers.
2️⃣ Sign the petition.
3️⃣ Share your censorship …
The court never heard the evidence, so we made the record public: twenty-five partner organizations and outlets, six programs, more than fifty bills tracked, a twenty-case lawfare docket, and the three things Congress must do about it. Launched on Constitution Day, in Washington.
https://sayerji.substack.com/p/they-dismissed-our-censorship-case
https://x.com/sayerjigmi/status/2102174217974219233?s=20
Before the first COVID shot, I warned that FDA’s own safety watchlist made rigorous post-market surveillance indispensable. We now know a key detection system had a documented blind spot. We also know senior officials were worrying about what safety caution would do to “public confidence”—and Meta records show pressure to restrict even true side-effect information. This is the record that changes the story.
Substack…
⚠️Universal postpartum home visits are voluntary. The immunization registry is not opt-out. A pending bill would eliminate religious school-vaccine exemptions. And Massachusetts has already linked home-visiting records with other state health data for evaluation.
https://sayerji.substack.com/p/massachusetts-is-building-a-public
On X: https://x.com/sayerjigmi/status/2101782379329421611?s=20
They Tried to Remove Natural Medicine from the Internet by ‘Killing its Messengers.’ I Used the Attacks to Supernova My Mission & Followed the Trail to the Epstein Files.
Share and view this presentation on X.
View on Substack: https://sayerji.substack.com/p/they-tried-to-remove-natural-medicine
They Tried to Remove Natural Medicine from the Internet by ‘Killing its Messengers.’ I Used the Attacks to Supernova My Mission & Followed the Trail to the Epstein Files.
Share and view this presentation on X.
View on Substack: https://sayerji.substack.com/p/they-tried-to-remove-natural-medicine
Release the files. Follow the evidence. Hold the powerful accountable.
Disclosure is only the beginning. We must understand how Epstein’s network operated, who enabled it, and what allowed abuse to go unpunished.
The purpose is bigger than exposing the past: it is protecting the vulnerable from those who still believe wealth and influence place them beyond accountability.
www.shadowempirebook.com
It took us 17 years to reach 100,000 studies.
Today, the rebuilt GreenMedInfo puts more than 450,000 indexed studies and 10,000 health topics within reach—and the research library is growing daily.
As we approach our 18th anniversary, we’ve rebuilt the platform from the ground up to make research on natural and integrative health easier to discover, explore, and share.
Start with a question. Search a condition, fo…
Sayer Ji, co-founder and Chairman of GWF has stood for many years at the intersection of natural health, scientific inquiry and free expression, paying a steep price for refusing to remain silent.
At the CHD Conference in Washington, D.C., Sayer shared how he was targeted and labeled a member of the so-called “Disinformation Dozen,” censored by major platforms and, more recently, subjected to invasive questioning by…
On stage at CHD’s DC conference today, someone on my panel asked what it really costs to challenge Pharma and the media that protects it. My answer — and it’s worse than most people realize:
🚨THEY KEEP CHANGING THE APOCALYPSE.
In 2020, Klaus Schwab warned that a major cyberattack could make COVID look like “a small disturbance.”
Then came Cyber Polygon.
Now it’s AI: “abrupt extermination”—and doomers who “don’t expect their children to make it to high school.”
But VP Vance just named the game: frontier AI companies begging Washington to regulate them looks like a Trojan horse.
Same machinery. New ap…
🔥27🤔7❤6💯1🤣1🤬1
Signed Sayer JI
Showing the 12 most recent of 99 posts we hold for @sayeregengmi. 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 edited after publishing
@sayeregengmi 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
24 August 2026
Most recent edit
24 August 2026
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 1 registered channel — 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 10 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.
Christiane Northrup M.D @drchristianenorthrup · 41,964 Telegram ranks this channel #3 of 79 here — alongside 78 others — read 29 August 2026
Health Nut News/Erin Elizabeth @erinhealthnutnews · 21,084 Telegram ranks this channel #4 of 81 here — alongside 80 others — read 24 September 2026
Jonathan Otto - Health Secret @jonathanottohealth · 29,337 Telegram ranks this channel #5 of 74 here — alongside 73 others — read 8 September 2026
Dr. Mercola @DoctorMercola · 31,490 Telegram ranks this channel #5 of 77 here — alongside 76 others — read 5 September 2026
The TRUTH About Cancer | Vaccines @TheTruthAboutCancer_Vaccines · 28,944 Telegram ranks this channel #9 of 74 here — alongside 73 others — read 8 September 2026
Mikki Willis Official @OfficialPlandemic · 33,413 Telegram ranks this channel #25 of 85 here — alongside 84 others — read 4 September 2026
Lee Merritt MD: FreedomDoc1 @FreedomDoc1 · 24,534 Telegram ranks this channel #33 of 75 here — alongside 74 others — read 16 September 2026
Children’s Health Defense @childrenshd · 53,485 Telegram ranks this channel #48 of 91 here — alongside 90 others — read 24 August 2026
Dr Joe Dispenza @officialdrjoedispenza · 31,412 Telegram ranks this channel #58 of 86 here — alongside 85 others — read 6 September 2026
Health Ranger @RealHealthRanger · 63,519 Telegram ranks this channel #77 of 82 here — alongside 81 others — read 21 August 2026
This channel appears in 10 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 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“GreenMedInfo” (@sayeregengmi), 27,942 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/sayeregengmi.
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.