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科学家发现控制结核病发生速度的基因

2005· article· zh· W3030117816 on OpenAlexaboutno aff
马洪明

Bibliographic record

VenueZhonghua yixue zazhi · 2005
Typearticle
Languagezh
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

有关细菌病原体感染的人类遗传易感性,人们知之甚少.由结核分支杆菌引起的结核病,在世界范围内仍然有较高的患病率和死亡率,每年新发结核病超过800万例,死亡200万例.来自加拿大McGill大学健康中心--宿主抵抗力研究中心的分子遗传学家Schurr估计有20亿人感染结核分支杆菌,约占世界总人口的1/3,但在感染人群中,仅有5%~10%的人在一生中患结核病,另外90%~95%的感染处于休眠状态,一生都不发病.在发病人群中,约有半数患者发生在感染后2年内,为快速发生的结核病,称为原发性结核病,这在儿童中特别常见,是其主要形式;而在感染2年后才缓慢进展出现临床症状的,叫做继发感染或再活化.这种从感染到发病存在速度差异的机制,人们还不清楚,他花费了5年的时间对此进行了研究,并于2005年8月23日在 (PNAS, 2005,102:12183-12188)上发表了他们的最新研究成果。

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.006
Scholarly communication0.0090.007
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0230.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.209
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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