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Record W2118501767 · doi:10.1007/s11932-996-0006-3

Current concepts in concussion rehabilitation

2004· review· en· W2118501767 on OpenAlexaff
Karen M. Johnston, Gordon A. Bloom, Jim Ramsay, James Kissick, David Montgomery, Dave Foley, Jen‐Kai Chen, Alain Ptito

Bibliographic record

VenueCurrent Sports Medicine Reports · 2004
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsRehabilitationConcussionAthletesMedicinePhysical medicine and rehabilitationPhysical therapyProcess (computing)Poison controlInjury preventionMedical emergencyComputer science

Abstract

fetched live from OpenAlex

Active rehabilitation of sport injuries is a concept familiar to athletes and those caring for them. Rehabilitation goals aim to optimize recovery efficiency and diminish chances of repeat injury. Rehabilitation programs take many aspects of recovery and wellness into consideration including physical, social, and psychologic components. Ultimately, this is important in the recovery process after concussion. In this article we introduce the largely unexplored concept of multidimensional concussion rehabilitation and discuss physical, psychologic, social, and sport-specific issues. As well, we propose future directions in this field.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.005

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 head0.124
GPT teacher head0.490
Teacher spread0.366 · 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
GenreReview

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

Citations74
Published2004
Admission routes1
Has abstractyes

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