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Record W4320916755 · doi:10.1093/pch/pxac116

Pediatric sport and exercise medicine: Eight things clinicians and patients should question

2023· article· en· W4320916755 on OpenAlexafffundabout
Kristin Houghton, Erika Persson, Laura Purcell

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsMcMaster UniversityUniversity of AlbertaUniversity of Alberta HospitalUniversity of British Columbia
FundersCanadian Academy of Sport and Exercise Medicine
KeywordsPhysical therapyMedicineSports medicineConcussionHarmManual therapyScoliosisPhysical medicine and rehabilitationAlternative medicineInjury preventionMedical emergencyPsychologyPoison controlSurgery

Abstract

fetched live from OpenAlex

Choosing Wisely Canada (CWC) is the national voice for reducing unnecessary tests and treatments in Canada. A small working group created by the Canadian Academy of Sport and Exercise Medicine developed a list of pediatric sport and exercise (SEM) recommendations based on existing research, experience, and common practice patterns. These recommendations identify tests and treatments commonly used in pediatric musculoskeletal assessments that are not supported by evidence and could expose patients to harm. Iterative feedback from key stakeholders informed the final list. The final list comprises eight recommendations including imaging recommendations for Osgood Schlatter's disease, shoulder and knee injuries, back pain, scoliosis, spondylolysis, distal radial buckle fractures, minor head injury/concussion, and management of chronic pain syndromes. Adopting these CWC pediatric SEM recommendations as part of routine practice may optimize care and minimize unnecessary investigations and treatments.

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)0.009
metaresearch head score (Gemma)0.041
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 score0.080
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0070.008
Open science0.0020.004
Research integrity0.0110.023
Insufficient payload (model declined to judge)0.0080.004

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 head0.014
GPT teacher head0.312
Teacher spread0.298 · 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
Published2023
Admission routes3
Has abstractyes

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