Predictors of prolonged recovery following acceptance for disability benefits: a systematic review of observational studies
Bibliographic record
Abstract
Objectives A significant number of disability claims remain active for substantial timeframes and account for a disproportionally large amount of financial resources. We conducted a systematic review of studies that explored predictors of prolonged recovery following acceptance for disability benefits in order to better inform early identification of claims at risk. Methods Eligible studies were observational studies that enrolled patients that were off work and in receipt of wage replacement benefits, and that explored variables associated with recovery. Teams of reviewers independently agreed on eligibility, assessed methodological quality, and extracted outcome data. All outcomes related to functional recovery were included and, when possible, we conducted meta-analyses. We planned, a priori, to perform stratified analyses according to whether studies evaluated functional outcome directly (eg, return to work) or by use of a surrogate, whether they did or did not meet various quality criteria, longer (1 year) versus shorter duration of follow-up, type of organisation proving benefits (government vs private), and shorter versus longer duration of the disabling complaint. Results We identified 3876 potentially eligible studies, and retrieved 167 studies in full text; 66 proved eligible. The chance-adjusted between-reviewer agreement (phi) on full text eligibility was 0.76. We anticipate data from subsequent stages of the project will be available at the time of the Conference. Conclusions Our findings should prove helpful for identifying disabled employees in receipt of benefits early in the claim process, which may facilitate more effective triaging of resources and improved outcomes.
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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 | 0.040 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".