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Record W4235985815 · doi:10.1093/pch/20.7.386-a

The author responds

2015· article· en· W4235985815 on OpenAlexaffabout
Karen E. Forward, Naveen Poonai, Gurinder Sangha, James A Seabrook, Tim Lynch, Rodrick Lim

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsChildren's Hospital of Western OntarioChildren’s Health Research InstituteWestern University
Fundersnot available
KeywordsComputer scienceMedicine

Abstract

fetched live from OpenAlex

Thank you Ms Scolnik for your insightful comments. However, we respectfully disagree that your suggestions would have led to a “deeper understanding” of the issues at hand. First, there is no evidence that Canadian parents are more likely to seek emergency medical care for female children than male children. Second, when examining the sports-injury literature, there is good evidence that males represent the overwhelming majority of children presenting to emergency departments with injuries (1). Third, since the implementation of the Hockey Canada National Safety Program in 1994, there have been major efforts to educate trainers and have them be present at all games involving both sexes and across all age groups (2). As emergency physicians and clinical investigators, our intent was to describe the injury patterns observed in female and male players and explore whether we could identify patterns that could lead to targeted injury prevention strategies. While it is important to elucidate factors that affect the decision to seek medical attention, the reasons highlighted do not reflect the societal shift in attitudes toward female sports over the past decade.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2015
Admission routes2
Has abstractyes

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