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Record W2133639356 · doi:10.2466/15.pms.118k12w2

Factors Associated with Self-Reported Arthritis 7 to 24 Years after a Traumatic Brain Injury

2014· article· en· W2133639356 on OpenAlexafffund
Sharon Ocampo-Chan, Elizabeth M. Badley, Deirdre Dawson, Graham Ratcliff, Angela Colantonio

Bibliographic record

VenuePerceptual and Motor Skills · 2014
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsBaycrest HospitalUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institute on AgingCanadian Institutes of Health Research
KeywordsTraumatic brain injuryMedicinePhysical therapyOccupational safety and healthInjury preventionArthritisCohortCohort studyPoison controlRetrospective cohort studyPopulationMusculoskeletal injuryMental healthPsychiatryEmergency medicineInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to profile characteristics of people with traumatic brain injury (TBI) who self-reported arthritis 7 to 24 yr. post-injury. Pre- and post-injury socio-demographic factors, injury-related factors, and postinjury standardized assessments measuring health, activity, and participation outcomes were assessed in a retrospective cohort study of 274 participants. The group self-reporting arthritis had significantly more sleep disturbances, poorer overall health, lower mental health and physical function, and decreased productivity. Also, they were older and reported a shorter length of loss of consciousness from TBI. These resulted suggest that musculoskeletal complaints from long-term survivors of TBI sholud be addressed in post-acute care and could guide future research on arthritis in the TBI population.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.270
Teacher spread0.245 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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