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Record W1504312926 · doi:10.1002/9781118914717.ch23

Emergency Medical Care in Paralympic Sports

2015· other· en· W1504312926 on OpenAlexaff
Peter Van de Vliet, Mike Wilkinson

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAthletesSequelaMedical careSports injuryHealth careSports medicineMedical emergencyAffect (linguistics)PsychologyMedicinePhysical therapyNursingPsychiatryPolitical science

Abstract

fetched live from OpenAlex

The assessment of risk of injury in paralympic sport is complicated by the different nature of impairments in the paralympic athlete. This chapter addresses both injury and illness types as well as impairment-related medical care. Athlete impairment types are known to affect injury characteristics, and advances in the use of assistive propulsion and protective devices as allowed by the sport-technical rules have brought an additional component of medical care, which must be included when planning emergency medical services in paralympic sports. Awareness of the risks of common medical problems experienced by paralympic athletes is essential to prevent serious sequela from unfortunate conditions and it is important for care staff to identify in advance the possible medical problems that athletes might face. The diversity of paralympic sports requires emergency medical services to be tailored to the needs and demands of each particular sport.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.003

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 head0.010
GPT teacher head0.318
Teacher spread0.308 · 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
GenreOther

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

Citations2
Published2015
Admission routes1
Has abstractyes

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