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Record W2409154174

[Results of a survey of community-acquired-pneumonia and evaluation of old and new Japanese Respiratory Society guidelines].

2007· article· en· W2409154174 on OpenAlexaff
Kazunori Gomi, Makoto Miki, Shigeru Itabashi, Tohru Kikuchi, Shinichi Miur, Toshiki Shikanai, Hiroshi Inoue, Kenichi Takeuchi, Akio Kanda, Shuzo Suzukio, Hideyuki Nakagawa, Mitsunobu Hommma, Hiroshi Miki, Tatsuya Abe, Katsushi Nishimaki, Hiroshi Saito, Hideo Yasugahir, Tsuneo Sayam, Makoto Sat, Ryo Kikuchi, Yoshihiro Honda, Akihiko Kawan, Akira Watanabe

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMedicinePneumoniaPneumonia severity indexCommunity-acquired pneumoniaInternal medicineIntensive care medicine
DOInot available

Abstract

fetched live from OpenAlex

We evaluated the usefulness of domestic and foreign guidelines for the diagnosis and treatment of patients with community-acquired-pneumonia at 23 institutions in 6 prefectures of the Tohoku Area, from December 2003 to November 2004. Based on the old and new Japanese Respiratory Society (JRS) guidelines, we evaluated severity, clinical efficacy and detection of atypical pneumonia. As for severity, the old guidelines led to the diagnosis of an excessive number of 'severe' cases. On the other hand, patients were appropriately diagnosed as having mild, moderate, severe, or very severe disease based on the new JRS guidelines (2005). The severity classification often correlated with the Pneumonia Severity Index (PSI) of the IDSA guidelines. The efficacy rate for patients who were prescribed the recommended drug according to the old JRS guidelines was 85.7% and for those who did not use the recommended drug it was 68.7% (p < 0.001).

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)0.002
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.248
GPT teacher head0.374
Teacher spread0.126 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicPneumonia and Respiratory Infections→French-language works237,207→