Percentage of pain intensity difference on an 11‐point numerical rating scale underestimates acute pain resolution
Bibliographic record
Abstract
BACKGROUND: A 50% reduction in pain intensity difference (50%PID) between baseline and follow-up evaluation is commonly accepted as adequate pain relief in emergency departments (EDs). However, 50%PID seems to be problematic with the 11-point numerical rating scale (NRS) since even baseline values are more divisible by 2 (50% reduction) than odd baseline values. This study evaluated the impact of this bias and integrated time between baseline and follow-up measurements, hypothesizing that the slope of relative pain intensity difference (SRPID) is a more accurate gauge of pain relief that can decrease bias and incorporate the time component of pain relief. METHODS: A post-hoc analysis of real-time data on an adult population from an urban ED identified 3199 consecutive patients who received an analgesic, had baseline NRS > 3 and a follow-up NRS within 2 h. Primary outcome was the percentage of patients with pain relieved from the 50%PID and the 50%SRPID criteria. RESULTS: Results showed that with 50%PID, even pain intensity levels on baseline NRS comprised a higher percentage of patients [60.7%; 95% confidence interval (CI): 58.8-63.0] with pain relief compared to odd pain intensity levels (51.7%; 95% CI: 48.8-54.6; p < 0.001), underestimating pain-relieved patients by 9% [95% CI: 0.05-0.13; effect size (ES) = 0.09]. The percentage of pain-relieved subjects with the 50%SRPID criteria was not affected by baseline NRS values (59.7% for whole sample; 95% CI: 58.0-61.4; ES = 0.02). CONCLUSIONS: The 50%PID method with an 11-point NRS for assessing adequate pain relief is significantly biased for specific baseline pain intensity level. In the particular context of ED acute pain, the SRPID seems less biased.
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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 | 0.031 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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; a candidate call from one teacher head, 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".