Evaluating acute pain intensity relief
Bibliographic record
Abstract
Percentage of pain intensity difference (PercentPID) is a recognized way of evaluating pain relief with an 11-point numerical rating scale (NRS) but is not without flaws. A new metric, the slope of relative pain intensity difference (SlopePID), which consists in dividing PercentPID by the time between 2 pain measurements, is proposed. This study aims to validate SlopePID with 3 measures of subjective pain relief: a 5-category relief scale (not, a little, moderate, very, complete), a 2-category relief question ("I'm relieved," "I'm not relieved"), and a single-item question, "Wanting other medication to treat pain?" (Yes/No). This prospective cohort study included 361 patients in the emergency department who had an initial acute pain NRS > 3 and a pain intensity assessment within 90 minutes after analgesic administration. Mean age was 50.2 years (SD = 19.3) and 59% were women. Area under the curves of receiver operating characteristic curves analyses revealed similar discriminative power for PercentPID (0.83; 95% confidence interval [CI], 0.79-0.88) and SlopePID (0.82; 95% CI, 0.77-0.86). Considering the "very" category from the 5-category relief scale as a substantial relief, the average cutoff for substantial relief was a decrease of 64% (95% CI, 59-69) for PercentPID and of 49% per hour (95% CI, 44-54) for SlopePID. However, when a cutoff criterion of 50% was used as a measure of pain relief for an individual patient, PercentPID underestimated pain-relieved patients by 12.1% (P < 0.05) compared with the SlopePID measurement, when pain intensity at baseline was an odd number compared with an even number (32.9% vs 45.0%, respectively). SlopePID should be used instead of PercentPID as a metric to evaluate acute pain relief on a 0 to 10 NRS.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".