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Record W4411040052 · doi:10.1016/j.cmicom.2025.105101

Re: Refining scores for non-invasive diagnosis of PCP by Soman et al.

2025· article· en· W4411040052 on OpenAlexaff
Emily G. McDonald, Connor Prosty, Benjamin Mappin‐Kasirer, Todd C. Lee

Bibliographic record

VenueCMI Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsSomanRefining (metallurgy)MedicineChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

We thank Soman and colleagues for their interest in our paper which contextualized the use of (1-3)-beta-D-glucan (BDG) for Pneumocystis jirovecii pneumonia (PCP) by using a clinical prediction rule [1]. We had previously published the derivation of this clinical prediction rule for patients undergoing bronchoscopy at our center for a suspected diagnosis of PCP [2], and proposed using the pretest probability to contextualize the Fungitell BDG cut points from our recent systematic review and meta-analysis [3].

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)0.015
metaresearch head score (Gemma)0.100
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: Commentary · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.100
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.033
GPT teacher head0.342
Teacher spread0.310 · 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
GenreCommentary

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
Published2025
Admission routes1
Has abstractyes

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