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Record W2054140919 · doi:10.1177/0008417412473262

Community integration outcomes after traumatic brain injury due to physical assault

2013· article· en· W2054140919 on OpenAlexfundvenueaboutno aff
Il Hwan Kim, Angela Colantonio, Deirdre Dawson, Mark Bayley

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsCommunity integrationTraumatic brain injuryRehabilitationMedicineInjury preventionPoison controlPopulationIntervention (counseling)Physical therapySuicide preventionOccupational safety and healthHuman factors and ergonomicsRetrospective cohort studyPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Community integration is considered an ultimate goal for rehabilitation after traumatic brain injury (TBI). PURPOSE: To determine (a) whether differences exist in rehabilitation outcomes between intentional and unintentional TBI populations and (b) whether TBI from assault is a predictor of community integration following inpatient rehabilitation. METHOD: Retrospective cohort study using population-based data from Canadian hospital administration records, 2001 to 2006. Outcome measure was the Reintegration to Normal Living Index (RNLI). FINDINGS: From a sample of 243 persons, 24 (9.9%) had sustained TBI from physical assault. Persons with TBI from physical assault reported significantly lower scores on two items on the RNLI's Daily Functioning subscale: "recreation" and "family role." IMPLICATIONS: These findings suggest that targeted intervention in these specific areas could be beneficial, which are often primarily addressed by occupational therapists in both inpatient rehabilitation and community settings.

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 score0.084
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.215
GPT teacher head0.440
Teacher spread0.225 · 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

Citations25
Published2013
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Occupational TherapySame topicTraumatic Brain Injury ResearchFrench-language works237,207