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Record W2325541282 · doi:10.1097/jom.0000000000000702

Comparing the Relationship Between Age and Length of Disability Across Common Chronic Conditions

2016· article· en· W2325541282 on OpenAlexaff
Arif Jetha, Elyssa Besen, Peter Smith

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsPublic Health OntarioUniversity of TorontoInstitute for Work & Health
FundersLiberty Mutual Research Institute for Safety
KeywordsMedicineDepression (economics)Diabetes mellitusChronic painDiseasePhysical therapyArthritisInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to compare the association between age and disability length across common chronic conditions. METHODS: Analysis of 39,915 nonwork-related disability claims with a diagnosis of arthritis, diabetes, hypertension, coronary artery disease, depression, low back pain, chronic pulmonary disease, or cancer. Ordinary least squares regression models examined age-length of disability association across chronic conditions. RESULTS: Arthritis (76.6 days), depression (63.2 days), and cancer (64.9 days) were associated with longest mean disability lengths; hypertension was related to shortest disability lengths (41.5 days). Across chronic conditions, older age was significantly associated with longer work disability. The age-length of disability association was most significant for chronic pulmonary disease and cancer. The relationship between age and length of work disability was linear among most chronic conditions. CONCLUSIONS: Work disability prevention strategies should consider both employee age and chronic condition diagnosis.

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)0.000
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 score0.006
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.107
GPT teacher head0.436
Teacher spread0.328 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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