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Record W1982952277 · doi:10.1002/ajim.20258

A study of factors influencing return to work after wrist or ankle fractures

2006· article· en· W1982952277 on OpenAlexafffundabout
Karen Seland, Nicola Cherry, Jeremy Beach

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Alberta
FundersWorkers' Compensation Board – Alberta
KeywordsMedicineAnkleOccupational safety and healthWork (physics)Physical therapyWristWorkers' compensationInjury preventionPoison controlProxy (statistics)Physical medicine and rehabilitationCompensation (psychology)Environmental healthSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Factors associated with time to return to work are poorly understood for occupational injuries, other than those to the back. METHODS: Anonymized data on claims for work-related wrist or ankle fracture between 1/1/1998 and 12/31/2002 were identified in administrative data held by the Workers Compensation Board in Alberta, Canada. Bivariate and Cox regression analyses were used to identify factors associated with return to work. RESULTS: Increased duration of temporary disability (TD) was associated with older age, female gender, work in construction and construction trade services, smaller company size, higher industry claim rates, a fall/jump from a height, ankle fracture, and greater medical aid costs in the 30 days following injury (used as a proxy for severity). CONCLUSIONS: Factors associated with longer time off work were largely consistent with those reported following back injury. Median time to return to work was longer following ankle than wrist fracture. Although Workers' Compensation Board (WCB) administrative data provided information that could be used to identify factors affecting return to work, better information on injury severity would considerably enhance their research potential.

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)0.002
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 score0.051
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.120
GPT teacher head0.481
Teacher spread0.360 · 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

Citations53
Published2006
Admission routes3
Has abstractyes

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