What Works Best for Whom?
Bibliographic record
Abstract
STUDY DESIGN: Exploratory subgroup analysis in a randomized controlled trial (RCT). OBJECTIVE: To detect possible moderators in the effectiveness of a workplace intervention in a population of workers with sick leave due to sub acute nonspecific low back pain. SUMMARY OF BACKGROUND DATA: In a recently published RCT, a workplace intervention was effective on return to work, compared to usual care. Examining the heterogeneity of effect sizes within the population in this RCT (n = 196) can lead to information on the effectiveness of the intervention in subgroups of patients. METHODS: A subgroup analysis was performed by adding interaction terms to the statistical model. Before analysis the following possible moderators for treatment were identified: age, gender, pain, functional status, heavy work, and sick leave in the previous 12 months. Cox regression analyses were performed and survival curves were plotted. RESULTS: The interaction (P = 0.02) between age (dichotomized at the median value) and the workplace intervention indicates a modifying effect. The workplace intervention is more effective for workers > or =44 years (HR, 95% CI = 2.5, [1.6, 4.1] vs. 1.2 [0.8, 1.8] for workers <44 years old). The interaction between sick leave in the previous 12 months and the workplace intervention is significant (P = 0.02). The intervention is more effective for workers with previous sick leave (HR, 95% CI = 2.8 [1.7, 4.9] vs. 1.3 [0.8, 2.0]). A modifying effect of gender, heavy work, and pain score and functional status on the effectiveness of this intervention was not found. CONCLUSION: The findings from these exploratory analyses should be tested in future RCTs. This workplace intervention seems very suitable for return to work of older workers and workers with previous sick leave. Gender, perceived heavy work, and baseline scores in pain and functional status should not be a basis for assignment to this intervention.
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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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".