[Can sick leave be predicted in patients with non-traumatic arm, neck and/or shoulder complaints?]
Bibliographic record
Abstract
OBJECTIVES: Impediments due to complaints of non-traumatic arm, neck and/or shoulder (CANS) during work often leads to consultation in primary care. This study examines the occurrence of sick leave among workers with new CANS, and evaluates sick leave trajectories and their characteristics. METHODS: This prospective 2-year cohort study included workers with a new CANS presenting in general practice. Participants filled out postal questionnaires on sick leave at 6-monthly intervals. Latent class growth mixture modelling was used to identify distinct trajectories of sick leave. Multinomial regression analyses identified characteristics of the subgroups. RESULTS: During follow-up, of the 533 participants 190 reported at least one episode of sick leave due to CANS. Three sick leave trajectories were distinguished: (a) 'low risk' trajectory (n = 366), with a constant low probability over time; (b) 'intermediate risk' trajectory, with a high probability at first consultation followed by a steep decrease in probability of sick leave (n = 122); (c) 'high risk' trajectory (n = 45), with a constant high probability of sick leave. Compared to the 'low-risk' trajectory, the other trajectories were characterised by more functional limitations, less specific diagnoses, more work-related symptoms and low coworker support. Specific for the 'high-risk' subgroup were more recurrent symptoms, more musculoskeletal comorbidity, high score on somatisation and low score on job demands. CONCLUSIONS: Three trajectories of sick leave were distinguished, graded from favourable to unfavourable. Several complaint-related and work-related factors and somatisation contributed modestly to identify an unfavourable trajectory of sick leave when presenting in primary care with CANS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".