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Record W4408343384 · doi:10.1111/nep.70016

Comment on: “The Comprehensive Incidence and Risk Factors of Fracture in Kidney Transplant Recipients: A Meta‐Analysis”

2025· article· en· W4408343384 on OpenAlexaboutno aff
Shubham Kumar, Ahmad Neyazi, Rachana Mehta, Ranjana Sah

Bibliographic record

VenueNephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisKidney transplantIncidence (geometry)Renal transplantKidney transplantationIntensive care medicineInternal medicineKidney

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.021
metaresearch head score (Gemma)0.159
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.038
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.159
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0050.006
Open science0.0080.003
Research integrity0.0380.042
Insufficient payload (model declined to judge)0.0150.012

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 head0.028
GPT teacher head0.292
Teacher spread0.264 · 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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