Telegram RegisterThe public register of Telegram

Channel

Doctor的诊断记录

@Doctor9527CJRebirth

On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Cite this entry

24,645subscribers

+707 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002108043838
TypeChannel
Username@Doctor9527CJRebirth
DescriptionDoctor的出击回忆录 老师形态各异,灵魂异常有趣
CreatedBetween 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live14 August 2026
Measurements held8
Confirmed unchanged1 time, most recently 14 August 2026
On Telegramt.me/Doctor9527CJRebirth

Growth

23,93824,64524,291.56 August 2026 — 23,938 subscribers7 August 2026 — 24,073 subscribers8 August 2026 — 24,164 subscribers9 August 2026 — 24,246 subscribers10 August 2026 — 24,288 subscribers11 August 2026 — 24,458 subscribers12 August 2026 — 24,514 subscribers14 August 2026 — 24,645 subscribers6 August 202614 August 2026
8 measurements spanning 7 days, net +707. 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 23,832–24,751 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
14 Aug 2026, 00:5524,645+131
12 Aug 2026, 17:0324,514+56
11 Aug 2026, 18:1424,458+170
10 Aug 2026, 15:3624,288+42
9 Aug 2026, 17:2224,246+82
8 Aug 2026, 18:4824,164+91
7 Aug 2026, 16:3124,073+135
6 Aug 2026, 20:0423,938first reading

Engagement

13 posts held, back to 2 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 24 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
31.9%
avg views ÷ 24,645 subscribers
Avg views / post
7,870
13 posts measured
Reaction rate
0.654%
reactions ÷ views · ER floor
Posts in window
13
of 13 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
WindowRolling 30 days · latest post in window 14 August 2026
Posts held13 (2 August 202614 August 2026)
Views total102,300
Reactions total669
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken15 Aug 2026, 05:34 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
926
Videos
195
Links
178

Lifetime counters from Telegram’s own channel header, read 15 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Video runtime
1m 44s
Average length
13s

Measured directly from 8 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

669 reactions across 13 posts, in 6 distinct kinds. The most used accounts for 51.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
34751.9%
👍27741.4%
🔥324.78%
🎉60.897%
🤡60.897%
👏10.149%

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 669reactions 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 2 August 2026 to 14 August 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.

Telegram Stars

Stars received
59
across the posts below
Posts paid on
9
of 13 we hold a reading for · 69%
Most on one post
12
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @Doctor9527CJRebirth. 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 13 most recent posts we hold for this entry, published 2 August 2026 to 14 August 2026. 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.

Recent posts

14 Aug 2026, 14:30 UTC≈3,010 views37 reactions6 Starsread 15 August 2026
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【花名】#哆啦 【车牌】@duolala55 【价格】8P/13PP #报成都学院写报告减1(前一周) 【验证】 Doctor 【评分】Doctor ’s体验分析报告→哆啦 :8.49 【硬件】23岁的成都妹妹,抖音粉丝40W+,小网红一枚,之前做直播带货,现在也下海了,今天见面的时候,和第一张照片一模一样,巨像以前我喜欢的大长腿理理,刚见面的时候,还以为是理理的亲姐姐,身高170,体重94左右,排骨精,大长腿,胸A,乳晕和豆豆小,全身无赘肉,屁屁有肉,后入撞击感强烈,腿细,无赘肉,全身皮肤介于冷白皮与黄皮之间,总之皮肤属于很白的一类,差一点冷白皮,后背有一个很小很小的纹身,乳晕和豆豆小且粉嫩,豆豆凸出的比较多,小荷才露尖尖角,肚子有较为明显的减肥痕迹,下面毛量偏多且浓密,外侧浅褐色,内侧粉嫩,舌吻很主动,女上的时候俯下身来主动舌吻,耳朵,脖子,胸,特别是脖子,巨敏感,水居多,真的就是符合周围那个表情包,很润,很滑,女

26👍9🔥2

14 Aug 2026, 03:00 UTC≈3,980 views56 reactions4 Starsread 15 August 2026
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最近又有很多假冒我的高仿号,我的唯一账号,@medical9527,可能只有l和I的区别,大家睁大眼睛,千万不要被骗; 第一,我不会私信任何人,看到私信和我一样头像的肯定是骗子,直接拉黑; 第二,也不会对任何人做任何推荐任何老师,遇到推荐的,肯定是骗子; 第三,我的账户号主页有我的“Doctor的诊断记录”个人频道挂在主页,骗子号是挂不了我的频道在主页显示; 第四,成都学院车库老师,凡单P定金超过100默认被盗号,若要求提前支付全款100%是骗子。凡遭“学院榜上已上牌老师”索要定金被骗的,保留好给定金时留言“成都学院”或约课记录有“成都学院”的截图,发送给学院管理,经学院管理确认无误后,由学院代为补偿100元定金,若被骗超过100定金学院概不负责!提高防骗意识,切忌精虫上脑!多读群里面【反诈】!如遇到索要定金超过100或要求提前付全款,立刻检查老师车牌号,若车牌号与车库相同,请立即联系Doctor或MrJ处理; 谨记,

👍3025🔥1

12 Aug 2026, 16:00 UTC≈6,390 views82 reactions6 Starsread 15 August 2026
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#kitty @kittyttm 那是在7月9号,我记得是一个大晴天,在前一天,果宝给我发了信息,她回来了,定在7月9号见面,9号上午kitty也给我发了信息,她也回来了,本来我想挑战一下我自己,可惜事与愿违,我败下阵来,我与kitty的约会,擦肩而过; 8月3号,她又一次休假回来,我与她,不能再次错过,虽与她相距甚远,但距离不能阻挡彼此的心; 随着一句我到了,按下电梯的向上键,看着电梯的数字慢慢变化,8、7、6…..1,叮~~我的预感完全准确,随着电梯门的打开,我们两人四目对视,果然是她; 2026年4月28-2026年8月4号,99个日夜,原来我们已经如此长的时间未见,时光业已变化一个季节,一个在电梯内,一个在电梯外,她挡住电梯,我们想许久未见的恋人,有初见的惊喜,也有许久未见的一点点陌生,她笑颜如花,我嘴角微笑,她牵着我,我牵着她,十指紧握; 啪,门被反锁,两人已经紧紧贴合在一起,彼此的思念,在此刻化作交织着低

👍4534🔥3

12 Aug 2026, 14:00 UTC≈5,830 views62 reactionsread 15 August 2026
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❤️‍🔥甜心新奇体验馆❤️‍🔥 🥰一个懂男人的专业服务馆、主营日本进口高级性爱娃娃体验 、主打新奇➕刺激 体验感直接拉满🔥 各区域有门店24小时接单、私信客服匹配就近门店安排体验。 🥳新奇体验超刺激➕超强夹吸➕云柔酥胸➕无限火力❤️‍🔥 欢迎搭乘新奇体验列车🚄 让我们一起探索未知领域🥳 一起享受快乐无边的体验🥳 客服: @TXSNXQTYG 群聊:@TXXQTYGJLQ 频道:@Txsnqg

👍2928🔥3🎉2

11 Aug 2026, 16:00 UTC≈6,320 views108 reactions12 Starsread 15 August 2026
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#果儿 @gyulngy 秋天的第一杯奶茶,是果宝给我买的,用她的话说,生活本就需要一点点仪式感; 这是践行我们一周见一次的约定(虽然没达成),但终究只能在有限的时间内抽出尽量多的时间互见,其实是我想见她; 这次的她,状态更好了,脸上的浮肿全部消了,因过敏长的豆豆也全部没了,光滑的脸蛋,更加的夺目; 随着电梯门缓缓的打开,我在电梯里,她在电梯厅,她穿着一个大露背的波点短裙,灯光照耀在她的脸上,她即使是平平淡淡的站在那,也能成为汹涌人潮的焦点,伸手挡住电梯,她小跑着向我走来; 此刻的思念,如汹涌的潮水袭面而来,也许在见她之前,思念未开,反倒是见了她之后,思念更加的澎湃; 正如了那句话,也许最深刻的思念就是,“高山上盖庙还嫌低,面对面坐着还想你;” 进门之后,放下她的包包,即可拥抱在一起,十余日未见的思念,全部化作两人最为浓烈且激情的热吻,可能只有如此,才能缓解我对她的想念,缠绵的吻,是两人舌头与口腔之间气息的交换,

52👍48🔥5🎉3

11 Aug 2026, 03:00 UTC≈6,750 views19 reactionsread 15 August 2026
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#成都最专业的外围经纪 为了大家不踩坑,我和陈伯 @zchenbo 每日深入虎穴,实体考察,实地体验,搜罗极品,防止大家踩坑😄 只要说出你的喜好,陈伯 @zchenbo 就能让你日到极品,再也不用开盲盒😂 如果不满意,陈伯不再晨勃😭 外围频道:https://t.me/+5TlRuLznbvc1NTNl 陈伯联系方式:@zchenbo 双向:@achenbobot

9👍7🔥3

10 Aug 2026, 04:00 UTC≈7,820 views34 reactionsread 15 August 2026
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文东嫩妹御姐~工作室❗️❗️ 只做精品嫩妹御姐,专做回锅,各种服务车,嫩妹车,御姐车,SM,双飞,多P,外出,包夜……应有尽有日批找文东不走弯路 ❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️❗️ 约课联系:➡️ @WD9852 选妃频道: https://t.me/nn3911 聊天双向群: https://t.me/nn93961

20👍10🔥3🎉1

8 Aug 2026, 16:00 UTC≈9,760 views126 reactions3 Starsread 15 August 2026
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【花名】#林汐 【车牌】@linxi4528 【价格】11P 成都学院不会上牌,因此没有报销 【验证】 Doctor 【评分】Doctor ’s体验分析报告→林汐 :8.16 【硬件】22岁的重庆妹妹,脸很小,人照8.6(看视频),网红脸,抖音主播,感觉是整容脸,问了结果不是,嘴角是小红书非常流行的那种标准网红嘴巴,身高166,体重82斤左右。细支硕果,乳晕和豆豆中等大小,蜂腰肥臀,胸B+,无科技,不下垂,很挺,腰部无赘肉,腰很细,屁股很翘,腿也很细,又是一个硬件战神,天赋异禀,整体皮肤颜色中等偏黄,手臂有小纹身,乳晕和豆豆颜色正常褐色,下面毛量中等,颜色中等褐色,掰开后粉嫩,进入后紧,包裹感明显,耳朵和胸很敏感,后入腰臀比自然下腰配合无敌的细腰,视觉冲击感强烈,传教完后入的时候能看到留了很多白浆; 【软件】蛇、三件套、制服、水中萧,浴室的AB面等课表内容 【体验】抖音女主播,约了好多次,终于得以见面; 白色超薄

62👍61🔥3

6 Aug 2026, 09:00 UTC≈12,500 views25 reactions8 Starsread 14 August 2026
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【花名】#知知 【车牌】@zhizhilaopo01 【价格】8P/13PP #报成都学院写报告减1 【验证】Mystery 【评分】Mystery’s体验分析报告→知知 :8.35 【硬件】22岁,河北廊坊人,人照8.5左右,喜爱JK,大眼睛,看着像嫩妹,年龄又算不上,介于嫩妹和御姐之间,身高160左右,体重90左右,胸A+,乳晕和豆豆正常大小、正常褐色,身材偏排骨,但稍稍有肉一点,全身无纹身,暖白皮,乳晕和豆豆正常褐色,下面毛面积较大,但比较稀疏,一线天,馒头逼,外侧颜色很浅,内侧很嫩; 【软件】舌吻、制服、舔胸舔蛋+三件套,会聊天,声音好听,本音就是那种略夹的音调,代聊也强调过时间必须拖够半小时才让客人走,123福音,已PUA代聊让妹妹再加点服务,后续期待狼友检验。 【体验】本来今天下午的时候已经约好了老师,医生锅锅突然来了任务,锅锅说这次应该是好的,我秉持半信半疑的态度,毕竟之前转身的太多了。怀着歉意水了

20👍3🔥2

5 Aug 2026, 11:59 UTC≈9,720 views34 reactions7 Starsread 12 August 2026
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#言 @yh188288 这是我和言,很早的约定; 自从上次一别,我们已约好下一次的见面; 人生本就须臾,既然喜欢,那就多见; 相约12点,在她家附近的酒店,上半身米白色超薄长袖针织,下半身浅绿色长裙,她就这样安静的坐在酒店门口大树的下面,没有艳丽的颜色,但也一眼望见她,走过去抱了抱她,两人已经无比熟稔,挽手一起步入酒店,印象最深刻的就是,用着她的小扇子,不停的说,好热,我给你扇一扇,优雅的言,在此刻展现了她可爱的一面; 路过酒店走廊,看到隔壁房间的窗外被树枝笼罩,她停下对我说,我好喜欢这样的房间,你喜欢,那我们下次见面就定这个房间,她笑颜如花,亲了我的脸蛋; 进入房间,亲亲抱抱,诉说对彼此的思念,我们见面不多,但早已牵挂彼此,她喜欢诉说她对生活的态度、对人生的感悟,她坐在窗前的沙发上,窗外的阳光透过窗纱均匀的洒在她的脸上,我的目光落在她的脸上,我安静的看着她说话,忽然感觉她的脸在发光哎,她认真说话的样子,真的好好

24👍9🔥1

2 Aug 2026, 16:00 UTC≈13,800 views57 reactions11 Starsread 12 August 2026
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【花名】#财财 【车牌】@caicaidc 【价格】6P/10PP 【验证】Mystery 【评分】Mystery’s体验分析报告→财财 :8.19 【硬件】19岁,床上白色背心照片就是本人,颜值中上的一个嫩妹,身高168,体重95左右,胸A,正常肤色,乳晕和豆豆正常大小、正常褐色,整体身材匀称,左手、右手都有纹身,背部大面积纹身,但不是精神小妹,下面毛量较多,逼型为蝴蝶,外侧正常褐色,内侧粉嫩,爱抽烟。 【软件】三件套,蛇因为口臭原因不想蛇,其他课表内容都有,做的时候配合度还是很高的,做的时候反馈强烈,小鸡鸡体验舒服,容易高潮,下面有点异味,传教时能闻到,自述因为快来姨妈导致,抽烟多,口腔略微有点臭,一点不想亲,建议少抽多漱口改善。 【体验】一开始看到医生锅锅发来的信息,心里是有点惊讶的。财财之前我是以普通客人身份去过的,后来被投诉下牌,现在又重新降价上牌。她应该也记得我,我问锅锅我去没问题吗,锅锅说没问题,那就

33👍21🔥3

2 Aug 2026, 13:00 UTC≈8,400 views14 reactionsread 8 August 2026
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Showing the 12 most recent of 13 posts we hold for @Doctor9527CJRebirth. 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

Citation-graph rank — 111,309 of 1,350,102entries 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.

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 10 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.

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 14 August 2026 — this entry's latest reading, not the date you are reading this.

“Doctor的诊断记录” (@Doctor9527CJRebirth), 24,645 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/Doctor9527CJRebirth.

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.