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Channel
@pathology_Savelov
On this record: Growth · Engagement · Reactions · Stars · Posts · Citations · Handles named that no longer answer · Cite this entry
3,447subscribers
-3 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001595197208 |
|---|---|
| Type | Channel |
| Username | @pathology_Savelov |
| Created | 23 December 2022 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 16 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 16 August 2026 |
| On Telegram | t.me/pathology_Savelov |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Aug 2026, 15:22 | 3,447 | -2 |
| 10 Aug 2026, 05:51 | 3,449 | -1 |
| 6 Aug 2026, 23:30 | 3,450 | no change |
| 6 Aug 2026, 08:18 | 3,450 | first reading |
16 posts held, back to 8 October 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 3 pagesof 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 16 posts for this entry, the most recent from 14 June 2025. An engagement rate over an empty window would be a number about nothing.
1,589 reactions across 16 posts, in 19 distinct kinds. The most used accounts for 33.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 534 | 33.6% | |
| 🔥 | 517 | 32.5% | |
| 👍 | 302 | 19.0% | |
| 🤣 | 135 | 8.50% | |
| 💯 | 23 | 1.45% | |
| 😁 | 20 | 1.26% | |
| ❤🔥 | 14 | 0.881% | |
| 🦄 | 8 | 0.503% | |
| 👏 | 7 | 0.441% | |
| 🏆 | 5 | 0.315% | |
| 👀 | 4 | 0.252% | |
| 🙏 | 4 | 0.252% | |
| 🤔 | 4 | 0.252% | |
| 🤯 | 3 | 0.189% | |
| 🥰 | 3 | 0.189% | |
| 🆒 | 2 | 0.126% | |
| 🤗 | 2 | 0.126% | |
| 💊 | 1 | 0.063% | |
| 😨 | 1 | 0.063% |
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 16 of the 16 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 1,589reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 8 October 2024 to 14 June 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @pathology_Savelov. 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 16 most recent posts we hold for this entry, published 8 October 2024 to 14 June 2025. 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.
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Я мало что знаю об ASCO. Да и ASCO не много знает обо мне. Но мы хотя бы друг о друге слышали.
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Signed Никита Савёлов
Пока Анастасия Данилова временно трудоустроена на более важном поприще, я за нее представляю результаты опроса о роли телеграм каналов для врачей-онкологов. Вывод: польза от каналов есть. Специалисты узнают что-то новое, меняют свои рутины подходы и чувствуют себя преобщенными к мировому сообществу. Так что не зря, оказывается, трудимся в поте лица в телеграм каналах!
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Signed Никита Савёлов
Вот так тихо и незаметно восходят новые звёзды… https://t.me/laumalishava
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Signed Никита Савёлов
Наконец мы с Даней записали разбор долькового рака молочной железы! Идея бродила давно и вот она выбродила. Почти год прошел от момента первого семинара в нашей команде до этой записи. Мы попытались показать, что дольковый рак уникальная клинико-морфологическая сущность, которую надо изучать отдельно от неспецифицированного рака. ❤️Ютуб Теория: https://youtu.be/sVg5yJNlBXY?si=M3C_PLBFQMOnVFXT Практика №1: https://yo…
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Signed Никита Савёлов
Школа выживания 2 (dMMR) За первую неделю после выпуска Школы выживания, посвященной определению dMMR/pMMR, на консультацию пришло несколько случаев, которые требуют специального обсуждения. Ситуации довольно интересные, не самые частые, хотя и неоднократно обсуждались в литературе. Причины первого из разбираемых феноменов до конца не ясны, но феноменологически он хорошо описан и понятно как его трактовать. У второг…
🔥41👍12❤9🥰3👏2💯2💊1
Signed Никита Савёлов
Школа выживания Долго меня пилила и пинала команда на эту тему, и мы наконец запустили этот проект - «Школа выживания». Он не про фундаментальную науку и сложные диагностические случаи. Тут нет курьёзов и загадок. Только алгоритмы принятия диагностических решений по наикратчайшему пути. Ютуб ВК Рутуб Запись: Михаил Москалец. Верстка видео и инфографика: Ариша.
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Signed Никита Савёлов
Эволюция с открытым концом Способность живых систем к репликации и “расширению” генома предоставляет им доступ к ранее недоступным степеням свободы (уровням организации), делая эволюцию процессом с открытым концом, а не быстрым процессом, ограниченным поиском локального оптимума на ландшафте приспособленности [1]. Что является финалом эволюции опухоли? Если бы человек жил вечно, какие формы опухолей мы теоретически …
🔥66👍11❤10
Signed Никита Савёлов
Патанатомия, основанная на правилах Возможно ли это? – Смотря что под этим понимать. Для чиновника это мечта! Для них правила в первую очередь связаны с возможностью подсчёта и расчётов – метрики, коэффициенты и проч. Но составить жёсткие и одновременно справедливые (полная оплата всех сделанных тестов) правила не получится по причине то, что назначение патологом тестов подчиняется не линейному, а стохастическому д…
🔥43👍18❤17👏1😨1
Signed Никита Савёлов
Полнота и непротиворечивость 2 Для любого свойства сложных систем характерно наличие нескольких паттернов реализации. Ясно что полнота и противоречивость не линейно нарастают по мере увеличения количества данных. Наверняка есть диагностические ситуации, где противоречивость обгоняет полноту и появляется с первых маркёров. А есть где новые маркёры всё дают и дают новые данные и полнота не скоро выходит на плато. Это …
👍33❤9
Signed Никита Савёлов
Полнота и непротиворечивость Невозможность полного и непротиворечивого описания сложных систем требует соблюдения баланса при выборе диагностических тестов. Нарушение баланса может привести к диагностической ошибке. Чем больше маркёров, тем полнее мы охарактеризуем систему, но тем противоречивей может быть результат. На рис. 1 биопсия из бронха курящего пациента 61 года. Видны поля клеток с морфологией клеток шипов…
🔥58👍21❤13🤯3🤔1
Signed Никита Савёлов
Ответ на комментарий Aleksandr к предыдущему посту. В этом блоге речь в основном идёт о патологоанатомической диагностике опухолей и некоторых смежных проблемах. Когда мы ставим диагноз той или иной опухоли, мы пользуемся признаками (не важно морфологическим или ИГХ) трёх видов: 1) валидированные, 2) «индустриальный» стандарт, 3) «приёмчики»: 1) Валидированные признаки – это признаки, правила оценки которых были ва…
❤64🔥32👍17❤🔥6🏆5🤔1
Signed Никита Савёлов
Showing the 12 most recent of 16 posts we hold for @pathology_Savelov. 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.
Citation-graph rank — 646,696 of 1,481,306entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
@pathology_Savelov named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
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 16 August 2026 — this entry's latest reading, not the date you are reading this.
“Ламповая онкопатология” (@pathology_Savelov), 3,447 subscribers as measured 16 August 2026. Telegram Register, tgregister.com/channel/pathology_Savelov.
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.