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Return to academic activities after concussion in sport

2017· article· en· W2617576278 on OpenAlexaffabout
Fanny Guérin

Bibliographic record

VenueBritish Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAnxietyCognitionIntervention (counseling)Physical therapyPhysical medicine and rehabilitationNeuropsychologyPsychologyMedicineClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Objective Identify the impact of postconcussion symptoms, cognitive change and psychologicalreactions on academic activities. Design Retrospective study on clinical evaluation and intervention; a prospective follow-up ofoutcome. Setting In a sport neuropsychogical clinic in Montreal, Quebec. Intervention The number and intensity of symptoms, the cognitivechanges (memory, attention), the psychological factors (anxiety, depression), and the delay after theconcussion are the independant variables. Outcome measures The capacity of following school part or full time is the dependant variable. Main results The noise and light sensitivity, the lack of cognitive endurance during learning, theattentional deficits and the performance anxiety in reaction to the difficulty to recover and to follownormally school activities compromise the academic functionning and force the students to quit school. Conclusions According to the results, a decision tool is created to help the evaluation and therecommandations of physician and neuropsychologist to individualise a return protocol to school. Competing interests The clinicial work of the author will be examined.

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)0.003
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 score0.092
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.360
Teacher spread0.320 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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