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Record W2810887412 · doi:10.1093/occmed/kqy091

Return to work after occupational and non-occupational lower extremity amputation

2018· article· en· W2810887412 on OpenAlex
W. Shane Journeay, Tim Pauley, Matthew Kowgier, Michael Devlin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOccupational Medicine · 2018
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsWest Park Healthcare CentrePublic Health OntarioUniversity of TorontoProvidence Health Care
Fundersnot available
KeywordsAmputationMedicineRehabilitationPhysical therapyRetrospective cohort studyCohort studyOdds ratioCohortPhysical medicine and rehabilitationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Factors impacting on return to work (RTW) after lower extremity amputation are important in occupational rehabilitation. AIMS: Our objective was to compare RTW in patients who had traumatic work-related amputation with amputations from other causes. METHODS: A retrospective cohort study was conducted with participants employed at the time of amputation and at least 1 year post-discharge from amputee rehabilitation. The primary outcome measure was RTW. RESULTS: One hundred and forty-seven amputees were included with 69% returning to work. Amputation reason did not impact on RTW (odds ratio [OR] 1.83, P = non-significant). Trans-femoral amputees were less likely to RTW (OR 0.22, P < 0.05). Years since amputation (OR 1.20, P = 0.001) and Reintegration to Normal Living Index (OR 1.05, P < 0.001) were predictive of RTW after adjusting for other covariates. CONCLUSIONS: Amputation aetiology did not impact on RTW. Years since amputation, level of amputation and Return to Normal Living Index were associated with RTW which may be important to consider in RTW prognosis and planning.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.105
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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