Back Pain Recurrence
Bibliographic record
Abstract
In Brief Study Design. Literature review. Objective. To present a framework for future analyses of back pain recurrence and explore the applicability and relevance of existing recurrence indicators. Summary of Background Data. Empirical studies of back pain have included a variety of indicators of recurrence, resulting in a range of findings about recurrence rates and associated factors. Little is known about the relationships between existing indicators. Methods. Literature overview, expert panel, and workshop discussion at the IX International Forum on Primary Care Research on Low Back Pain. Results. Using the International Classification of Functioning, Disability, and Health (ICF), disabling back pain was conceptualized as a health condition, i.e., back pain disorder (BPD), and BPD recurrence was conceptualized as involving a return of atypical back pain and/or back-pain-related difficulty performing tasks and actions related to the initial episode. Using the ICF, 2 types of recurrence indicators were identified: those directly describing components of BPD and those indirectly doing so (e.g., recurrence of health care utilization). Conclusion. In light of the difficulty in measuring BPD recurrence, transparent definitions and a clear understanding of the implications of using particular indicators is required. Future research should focus: on examining the capture BPD recurrence by various research instruments, improving understanding of the relationship between indicators, and gaining insight into how individuals experiencing BPD view recurrence. Disabling back pain was conceptualized as a health condition–back pain disorder (BPD). This allowed focusing on aspects of individuals' experience: impairment, activity limitations, and participation restrictions. Recurrence of BPD was defined as any postinitial episode. Two types of recurrence indicators were identified: describing components of BPD directly and indirectly.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".