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Record W2055370651 · doi:10.1080/02699050701201540

The efficacy of acquired brain injury rehabilitation

2007· review· en· W2055370651 on OpenAlexaff
Nora Cullen, Josie Chundamala, Mark Bayley, Jeffrey W. Jutai

Bibliographic record

VenueBrain Injury · 2007
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern UniversityUniversity of TorontoToronto Rehabilitation Institute
Fundersnot available
KeywordsRehabilitationAcquired brain injuryPhysical medicine and rehabilitationTraumatic brain injuryMedicinePsychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this review was to investigate the efficacy of rehabilitation interventions in acquired brain injury (ABI) rehabilitation to provide guidance for clinical practice based on the best available evidence. METHODS AND MAIN OUTCOMES: A systematic review of the literature from 1980-2005 was conducted focusing on rehabilitation interventions for ABI. The efficacy of a given intervention was classified as strong (supported by at least two randomized controlled trials (RCTs)), moderate (supported by a single RCT), or limited (supported by other types of studies in the absence of RCTs). RESULTS: The majority of interventions were only supported by limited evidence. However, there is moderate evidence that inpatient rehabilitation results in successful return to work and return to duty for the majority of military service members, increasing the intensity of rehabilitation reduces length of stay and improves short-term functional outcomes, and that direct patient involvement in neurorehabilitation goal setting results in significant improvements in reaching and maintaining those goals. CONCLUSIONS: There is a need for studies of improved methodological quality into ABI rehabilitation.

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)0.013
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0070.004
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.107
GPT teacher head0.446
Teacher spread0.339 · 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 designSystematic review
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

Citations98
Published2007
Admission routes1
Has abstractyes

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