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Record W2980678404

Medical Scope:ぶどう膜炎におけるTNF阻害薬使用に伴う感染症リスク管理

2019· article· ja· W2980678404 on OpenAlexvenueno aff
岩田 大樹, ほか

Bibliographic record

VenuePharma Medica · 2019
Typearticle
Languageja
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)MedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

ぶどう膜炎に対する生物学的製剤の使用が本格的に始まったのは,2007年にベーチェット病の難治性網膜ぶどう膜炎に対してインフリキシマブが保険適用となってからである。優れた有効性を有し,ベーチェット病の視力予後を大きく改善させた。2016年9月には非感染性ぶどう膜炎にアダリムマブが保険適用となり,TNF阻害薬はぶどう膜炎治療において重要な位置を占めるに至っている。使用にあたっては感染性ぶどう膜炎との鑑別を行い,適応を見極めることが非常に重要となり,使用中には重篤な感染症などの有害事象に対して十分な注意と適切な対処が必要となる。本稿ではTNF阻害薬使用に伴う感染症の管理について述べる。

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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