Development of a metric for a day of manageable pain control: derivation of pain severity cut-points for low back pain and osteoarthritis
Bibliographic record
Abstract
The objective of this study was to adapt the concept of 'episode-free day', a metric for measuring symptom relief in daily units, to the clinical outcome literature for persistent pain. The episode-free day metric is widely used in other medical literature, but no analogous measure exists in pain literature. Prior focus groups with this population suggested that a 'Day of Manageable Pain Control' was an appropriate name for the metric. In the present study, in order to derive a statistical criterion for 'Manageable Day', we used Serlin et al.'s (Pain 61 (1995) 277) cut-point derivation method to derive a single cut-point on a 0-10 scale of average pain that divided groups with significant persistent pain optimally on pain-related functional interference. Participants were 194 patients with moderate-severe low back pain (n=96) or osteoarthritis (n=98). For both patient samples, '5' was the cut-point that optimally distinguished groups on pain-related interference. '5-8' and '5-7' were double cut-point solutions that optimally divided LBP and OA samples into three categories (e.g. lowest, medium and highest average pain), respectively. Derived cut-points were confirmed using a variety of measures of functional disability. Together with research that showed that average pain ratings of approximately 5 and below permit increased function and quality of life in patients with moderate to severe low back pain and osteoarthritis, our findings provide support for the use of 0-5 on a 0-10 numeric average pain severity scale as one possible criterion for a Manageable Day.
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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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".