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Record W1977016000 · doi:10.1097/htr.0000000000000066

Quality of Guidelines for Cognitive Rehabilitation Following Traumatic Brain Injury

2014· article· en· W1977016000 on OpenAlexaff
Peter Bragge, Loyal Pattuwage, Shawn Marshall, Veronica Pitt, Loretta Piccenna, Mary Stergiou‐Kita, Robyn Tate, Robert Teasell, Catherine Wiseman‐Hakes, Ailene Kua, Jennie Ponsford, Diana Velikonja, Mark Bayley

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsToronto Rehabilitation InstituteWestern University
Fundersnot available
KeywordsIntraclass correlationRehabilitationCognitionGuidelineAuditMedicineStakeholderQuality (philosophy)Applied psychologyPsychologyPhysical therapyClinical psychologyPsychiatryPsychometricsBusinessPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Cognitive rehabilitation following traumatic brain injury can aid in optimizing function, independence, and quality of life by addressing impairments in attention, executive function, cognitive communication, and memory. This study aimed to identify and evaluate the methodological quality of clinical practice guidelines for cognitive rehabilitation following traumatic brain injury. METHODS: Systematic searching of databases and Web sites was undertaken between January and March 2012 to identify freely available, English language clinical practice guidelines from 2002 onward. Eligible guidelines were evaluated using the validated Appraisal of Guidelines for Research and Evaluation II instrument. RESULTS: The 11 guidelines that met inclusion criteria were independently rated by 4 raters. Results of quality appraisal indicated that guidelines generally employed systematic search and appraisal methods and produced unambiguous, clearly identifiable recommendations. Conversely, only 1 guideline incorporated implementation and audit information, and there was poor reporting of processes for formulating, reviewing, and ensuring currency of recommendations and incorporating patient preferences. Intraclass correlation coefficients for agreement between raters showed high agreement (intraclass correlation coefficient > 0.80) for all guidelines except for 1 (moderate agreement; intraclass correlation coefficient = 0.76). CONCLUSION: Future guidelines should address identified limitations by providing implementation information and audit criteria, along with better reporting of guideline development processes and stakeholder engagement.

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)0.243
metaresearch head score (Gemma)0.627
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2430.627
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0170.018
Science and technology studies0.0020.003
Scholarly communication0.0080.005
Open science0.0060.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.333
GPT teacher head0.577
Teacher spread0.244 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainEvaluation
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

Citations33
Published2014
Admission routes1
Has abstractyes

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