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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".