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Record W4391784760 · doi:10.1007/s00247-024-05855-2

Pre- and post-COVID-19 gender trends in authorship for paediatric radiology articles worldwide: a systematic review

2024· review· en· W4391784760 on OpenAlexaff
Rakhshan Kamran, Liam Jackman, Cynthia Chan, Ann C Lee, Aleeza Kamran, Chloë Jacklin, Eve Deck, Yujin Suk, Victoria Jackman, Micon Garvilles, Susan C. Shelmerdine, Andréa S. Doria

Bibliographic record

VenuePediatric Radiology · 2024
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsHospital for Sick ChildrenMemorial University of NewfoundlandWestern UniversityUniversity of OttawaMcMaster UniversityUniversity of WaterlooSickKids FoundationUniversity of Toronto
FundersMedical Research CouncilNational Institute for Health and Care Research
KeywordsMedicineScopusCoronavirus disease 2019 (COVID-19)MEDLINEDemographyPsychological interventionMeta-analysisFamily medicineSystematic reviewPathologyDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.014
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.102
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0150.020
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.093
GPT teacher head0.394
Teacher spread0.301 · 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.

Study designSystematic review
DomainIncentives
GenreReview

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
Published2024
Admission routes1
Has abstractno

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