Mitigating Persistent Symptoms Following Rehabilitation in Musculoskeletal Disorders
Bibliographic record
Abstract
BACKGROUND: The majority of patients with musculoskeletal pain (62% to 64%) achieve their treatment goals upon completing rehabilitation. However, a high re-consultation rate after discharge is frequently reported. Numerous authors have recognized the necessity of secondary prevention programs (after-discharge strategy) to ensure that the gains are maintained or further pursued after the completion of a rehabilitation program. Little is known about the different strategies currently in use, and a detailed review of the existing strategies is needed for future integration into the healthcare systems. OBJECTIVE: This review systematically scopes and synthesizes the after-discharge strategies reported in the literature following rehabilitation for individuals experiencing musculoskeletal pain. METHODS: Four databases (OVID MEDLINE, EMBASE, Web of Sciences, and OVID PsycInfo) were screened from their inception until May 4, 2023. Literature search, screening, and extraction were performed according to the PRISMA extension for scoping review guidelines. RESULTS: Different after-discharge strategies were identified and grouped into 2 main categories: (1) in-person and (2) remote strategies. In-person strategies included (1.1) in-person booster sessions and (1.2) the use of existing community programs after discharge. Remote strategies included remote strategies that (2.1) involve a health care professional service or (2.2) strategies that do not involve any health care professional service. DISCUSSION: We identified various after-discharge strategies designed to sustain gains and improve patients' self-management skills following the completion of a rehabilitation program. The existence of numerous promising strategies suggests their potential suitability for various contexts.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".