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Record W2792291562 · doi:10.1080/02699052.2018.1429662

Analysis of serum cortisol to predict recovery in paediatric sport-related concussion

2018· article· en· W2792291562 on OpenAlexafffund
EV. Ritchie, Carolyn A. Emery, Chantel T. Debert

Bibliographic record

VenueBrain Injury · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsHotchkiss Brain InstituteOntario Brain InstituteAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsConcussionMedicineHydrocortisoneTraumatic brain injuryInjury preventionPoison controlPsychologyPhysical therapyPhysical medicine and rehabilitationInternal medicineEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the relationship between acute serum cortisol following pediatric sport-related concussion (SRC) and clinical outcome measures of symptom burden and length to return to sport (RTS) Methods: Prospective observational study of ice hockey players ages 11-12 recruited prior to the hockey season. Players sustaining a SRC were assessed by a sports medicine physician completed a child Sport Concussion Assessment Tool-3 (childSCAT-3) and serum cortisol samples. RESULTS: Of 636 ice hockey players enrolled, 41 sustained a SRC. In total, 22 serum cortisol samples were collected, with 14 (63.6%) meeting inclusion criteria. Four players presented with abnormally low cortisol and were more likely to experienced more symptoms (17.8 ± 1.9 vs. 7.5 ± 6.0) more severe symptoms (28.5 ± 5.8 vs. 10.2. ±8.8) and took longer RTS (23 ± 13.6 vs. 14.0.7 ± 7.9.). CONCLUSION: Paediatric ice hockey players following SRC with abnormally low cortisol may be more susceptible to experiencing increase symptom burden and take longer to return to sport than players with population-based normal cortisol.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.330
Teacher spread0.305 · 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 designObservational
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

Citations19
Published2018
Admission routes2
Has abstractyes

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