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 12 September 2026 and assigned it the closest of 31 fixed categories, at 96% 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.
13 measurements spanning 43 days, net -78. 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 6,530–6,632 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
19 Sept 2026, 17:20
6,542
-15
14 Sept 2026, 20:38
6,557
-9
10 Sept 2026, 23:01
6,566
+3
6 Sept 2026, 05:40
6,563
-8
2 Sept 2026, 10:23
6,571
-5
30 Aug 2026, 05:34
6,576
-8
27 Aug 2026, 12:55
6,584
-6
24 Aug 2026, 03:23
6,590
-4
17 Aug 2026, 10:56
6,594
-5
14 Aug 2026, 01:18
6,599
-7
11 Aug 2026, 01:44
6,606
-14
7 Aug 2026, 14:46
6,620
no change
7 Aug 2026, 14:39
6,620
first reading
Engagement
13 posts held, back to 12 May 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 9 pages of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 13 posts for this entry, the most recent from 27 June 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
56s
Average length
56s
Measured directly from 1 video 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
56 reactions across 13 posts, in 7 distinct kinds. The most used accounts for 35.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🦄
20
35.7%
❤
10
17.9%
🔥
8
14.3%
👎
6
10.7%
🐳
5
8.93%
😡
4
7.14%
🕊
3
5.36%
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 13 of the 13 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 56 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 13 most recent posts we hold, published 12 May 2026 to 27 June 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.
Готова запись моей лекции для собственников и управляющих клиник
После просмотра вы узнаете:
— главную задачу собственника клиники
— способы привлечения пациентов в клинику
— роль куратора в клинике
— как работают воронки продаж в соцсетях для бесплатного привлечения пациентов
Смотрите запись здесь 👉 https://t.me/+1dUb0WcnUfBlZmVi
Через неделю пройдет онлайн-лекция «Как клинике привлекать пациентов?»
Приглашаю собственников клиник, управляющих и всех, кто отвечает за привлечение пациентов — обсудить:
— как в нынешних реалиях привлекать больше пациентов
— как управлять потоком обращений и записей
— какие ошибки мешают формировать полную запись
— как выстроить систему, которая помогает получать новых пациентов на регулярной основе
❗️25 июня в …
❗️В это воскресенье провожу онлайн-лекцию для врачей и владельцев клиник
Поделюсь своим опытом и расскажу, как использовать соцсети для формирования стабильного потока пациентов!
🗓️7 июня в 13:00 по мск
Приходите онлайн, чтобы задать свои вопросы по медицинскому контенту и личному бренду в социальных сетях!
Лекция пройдет в этом канале, вступайте по ссылке👉🏻 https://t.me/+rht_sI3GWxw1MzA6
Предприниматели нового времени
Успех бизнеса определяется не только прибылью, но и пользой для людей. Многие предприниматели вкладывают свои силы и средства в то, чтобы делать жизнь вокруг лучше: помогают семьям, поддерживают образование, заботятся об экологии.
Об этом редко пишут в СМИ и соцсетях. Поэтому партия «Новые люди» учредила премию «Предприниматели нового времени», чтобы сказать спасибо тем, кто меняет ст…
Сегодня в 18:00 по мск проведу открытые онлайн-разборы для врачей и владельцев клиник
Обычно такие разборы происходят лично и остаются «за кадром», но сегодня я решила показать всё публично! Мы с командой выбрали 4 разных ситуации:
— владелец офтальмологической клиники
— управляющая стоматологии с блогом на 34 подписчика
— владелец стоматологической клиники
— хирург-гинеколог в найме
Я проведу онлайн эфир и в форма…
Мы продолжаем популяризировать микробиологию. На днях сняли подкаст с самым знаменитым стоматологом в СНГ — Луизой Автандилян
Обсудили связь ротовой полости в здоровье всего остального организма. Разговор получился очень содержательным, будем постепенно выкладывать!
Сейчас пациенты ищут специалиста в соц.сетях, а не на сайтах клиник
Коллеги, времена поменялись и пациентам сейчас важны не только профессионализм специалиста, но и то как он общается, как устроен сервис в его клинике, какое общее впечатление специалист создает
👉🏼 поэтому сейчас пациенты идут знакомиться с вами не на сайте, а формируют доверие к вам через экран и контент в ваших соц.сетях
Записала для вас новый по…
🔥3🦄1
Showing the 12 most recent of 13 posts we hold for @smilestudio_doctorLu. 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.
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 3 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.
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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“Луиза Автандилян” (@smilestudio_doctorLu), 6,542 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/smilestudio_doctorLu.
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.