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Record W2031297858 · doi:10.2340/16501977-0783

Differential profiles for patients with traumatic and non-traumatic brain injury

2011· article· en· W2031297858 on OpenAlexafffundabout
Angela Colantonio, G Gerber, Mark Bayley, Raisa Deber, Hanna Kim

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWest Park Healthcare CentreToronto Rehabilitation InstituteUniversity of Toronto
FundersToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term CareOntario Neurotrauma Foundation
KeywordsTraumatic brain injuryRehabilitationMedicinePhysical therapyEtiologyInjury preventionAcquired brain injuryPhysical medicine and rehabilitationPoison controlEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To profile the demographic, clinical and environmental characteristics of persons with acquired brain injury receiving inpatient rehabilitation services in Canada. DESIGN: This study utilizes data from the Canadian Institute for Health Information's National Rehabilitation Reporting System, between April 2001 and March 2006. The data were collected from publicly insured institutions providing inpatient rehabilitation across Canada. The main outcome measures examined were demographic and clinical characteristics. PARTICIPANTS: Adults with brain injury by traumatic (n=2675) vs non-traumatic causes (n=2759). RESULTS: Approximately half of acquired brain injury patients receiving inpatient rehabilitation had non-traumatic causes of brain injury. Traumatic brain injury patients were more likely to be younger, male, from rural areas, and to make greater gains in rehabilitation. Differences were found in the types and numbers of comorbidities. However, patients from these 2 groups had similar lengths of rehabilitation stay. CONCLUSION: These findings support a differential profile of patients by brain injury aetiology. This has relevance for staff training, resource allocation and future research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.257
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.340
Teacher spread0.293 · 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 teacher head, 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

Citations41
Published2011
Admission routes3
Has abstractyes

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