Channel
@jinshizhongjing
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
130subscribers
-24 since we began measuring on 6 September 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003892129440 |
|---|---|
| Type | Channel |
| Username | @jinshizhongjing |
| Created | 27 July 2026 — measured — dated from the channel’s first post |
| First recorded | 9 September 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/jinshizhongjing |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 18 Sept 2026, 22:17 | 130 | -15 |
| 9 Sept 2026, 13:37 | 145 | -9 |
| 9 Sept 2026, 09:23 | 154 | no change |
| 6 Sept 2026, 19:47 | 154 | first reading |
8 posts held, back to 27 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 1 page of Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 9 September 2026 |
|---|---|
| Posts held | 8 (27 July 2026 – 9 September 2026) |
| Views total | 1 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 9 Sept 2026, 09:23 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.
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1 | 100.0% |
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 1 of the 8 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 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 8 most recent posts we hold, published 27 July 2026 to 9 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.
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《30岁男人最难熬的几年：身体的问题，最后变成了心理的问题》 【匿名案例整理】 男，32岁。 刚开始出现问题的时候，其实没有太在意。 那时候工作压力大，经常熬夜，应酬也多。 第一次发现状态不如以前，还安慰自己： “可能只是太累了，休息几天就好了。” 可是慢慢发现，并没有那么简单。 从偶尔一次，到越来越频繁。 最开始害怕的是身体变化，后来害怕的是自己的心理变化。 以前性格比较开朗，后来变得越来越敏感。 朋友聚会不太想参加，甚至有时候刻意回避亲密关系。 最难受的一句话是： “我才30多岁，为什么感觉自己像提前衰退了一样？” 这种事情又不好跟别人说。 男人有时候就是这样。 身体上的问题还能忍，但心理上的压力越来越大。 网上查资料，看各种说法。 有人说肾虚，有人说压力大，有人说必须马上解决。 越看越焦虑。 也尝试过一些方法，但是没有真正弄清楚自己的问题在哪里。 后来接触中医调理。 第一次沟通的…
❤1
今世仲景｜中医破局者｜男科·妇科问题调理 pinned «我出生于中医文化环境之中，自幼接触中医，并将其作为一生钻研的方向。 多年研习经典，深入探索《黄帝内经》《神农本草经》《伤寒论》《金匮要略》等医学根源，同时广泛学习历代医家的学术思想与临床经验。 在学习过程中，我研读李可、叶天士、傅青主、蒲辅周、张锡纯、刘渡舟、左季云等诸多名家经验，在不同学术体系中不断吸收、验证、融合，形成自己的临床认识。 我始终认为，中医贵在传承，更贵在领悟。 我不盲从古人，也不轻视传统。真正的传承，不是把古人的话背下来，而是理解其中的规律，并用这些规律解决今天患者遇到的问题。 …»
我出生于中医文化环境之中，自幼接触中医，并将其作为一生钻研的方向。 多年研习经典，深入探索《黄帝内经》《神农本草经》《伤寒论》《金匮要略》等医学根源，同时广泛学习历代医家的学术思想与临床经验。 在学习过程中，我研读李可、叶天士、傅青主、蒲辅周、张锡纯、刘渡舟、左季云等诸多名家经验，在不同学术体系中不断吸收、验证、融合，形成自己的临床认识。 我始终认为，中医贵在传承，更贵在领悟。 我不盲从古人，也不轻视传统。真正的传承，不是把古人的话背下来，而是理解其中的规律，并用这些规律解决今天患者遇到的问题。 在临床中，我接触较多男性健康与女性体质调理相关问题。对于阳痿、早泄、男性精力不足，以及女性痛经、气血不足、体质失衡等问题，我并不认为这些是所谓“无法解决”的问题。 很多时候，患者面对的并不仅仅是身体上的困扰，更是长期没有找到正确方向后的焦虑和无助。 我不喜欢把简单的问题复杂化，也不喜欢利用患者的恐惧放大疾病。身体出现问题，…
Channel name was changed to «今世仲景｜中医破局者｜男科·妇科问题调理»
Channel photo updated
Channel created
Showing the 8 most recent of 8 posts we hold for @jinshizhongjing. 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.
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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“今世仲景｜中医破局者｜男科·妇科问题调理” (@jinshizhongjing), 130 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/jinshizhongjing.
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.