Number of physiotherapy sessions in work-related absenteeism due to musculoskeletal disorders, by gender, age and occupation: A retrospective cohort study
Bibliographic record
Abstract
BackgroundThe number of physiotherapy sessions needed to treat musculoskeletal conditions varies in the literature; age and gender may partly explain the discordant reports. However, no research has analysed whether occupation may influence this outcome in the working population.ObjectivesTo assess the number of physiotherapy sessions performed for low back pain (LBP), cervicalgia (CG), and whiplash syndrome (WS) in workers on sickness absence, according to gender, age, and occupation.MethodsIn this retrospective cohort study, the outcome variable was the number of physiotherapy sessions needed to recover from LBP, CG, and WS. Explanatory variables were sex, age, occupation, year when physiotherapy ended, and treatment centre. The adjusted median differences in the number of sessions (MDa) were calculated.ResultsOlder workers (55-65 years) needed a median of 2.6 additional sessions for LBP, 3.0 more sessions for CG, and 3.6 for WS. Men underwent fewer sessions than women (LBP and CG: MDa -0.9 sessions; WS: MDa -1.7 sessions). Compared to crafts and related trades workers, plant and machine operators and assemblers required more sessions to recover from LBP (MDa 0.7), as did service and sales workers (MDa 0.7). In CG and WS, differences were observed for technicians and associate professionals (MDa 1.3 and MDa 1.7, respectively), and for professionals (MDa 2.4 and MDa 1.6). Clerical support workers also needed significantly more sessions for CG.ConclusionsThe number of sessions required to recover from LBP, CG, and WS in workers on work-related sickness absence is different according to gender, age, and occupation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.000 |
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.
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".