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Record W2111279030 · doi:10.1080/16501960410023877

Methodological issues and research recommendations for mild traumatic brain injury: the who collaborating centre task force on mild traumatic brain injury

2004· article· en· W2111279030 on OpenAlexaff
Linda Carroll, J. David Cassidy, Lena W. Holm, Jess F. Kraus, Victor G. Coronado

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTraumatic brain injuryMedicineTriageSystematic reviewInjury preventionPoison controlPopulationTask forcePhysical medicine and rehabilitationTask (project management)PsychologyMEDLINEIntensive care medicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

The WHO Collaborating Centre for Neurotrauma Task Force on Mild Traumatic Brain Injury performed a comprehensive search and critical review of the literature published between 1980 and 2002 to assemble the best evidence on the epidemiology, diagnosis, prognosis and treatment of mild traumatic brain injury. Of 743 relevant studies, 313 were accepted on scientific merit and comprise our best-evidence synthesis. The current literature on mild traumatic brain injury is of variable quality and we report the most common methodological flaws. We make recommendations for avoiding the shortcomings evident in much of the current literature and identify topic areas in urgent need of further research. This includes the need for large, well-designed studies to support evidence-based guidelines for emergency room triage of children with mild traumatic brain injury and to explore more fully the issue of prognosis after mild traumatic brain injury in the elderly population. We also advocate use of standard criteria for defining mild traumatic brain injury and propose a definition.

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)0.640
metaresearch head score (Gemma)0.868
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6400.868
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.0300.029
Science and technology studies0.0070.013
Scholarly communication0.0170.014
Open science0.0130.008
Research integrity0.0170.016
Insufficient payload (model declined to judge)0.0050.002

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 head0.342
GPT teacher head0.530
Teacher spread0.188 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations1,025
Published2004
Admission routes1
Has abstractyes

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