Comparing pain intensity rating scales in acute postoperative pain: boundary values and category disagreements
Bibliographic record
Abstract
Pain intensity assessment scales are important in evaluating postoperative pain and guiding management. Different scales can be used for patients to self-report their pain, but research determining cut points between mild, moderate and severe pain has been limited to studies with < 1500 patients. We examined 13,017 simultaneous acute postoperative pain ratings from 913 patients taken at rest and on activity, between 4 h and 48 h following surgery using both a verbal rating scale (no, mild, moderate or severe pain) and 0-100 mm visual analogue scale. We determined the best cut points on the visual analogue scale between mild and moderate pain as 35 mm, and moderate and severe pain as 80 mm. These remained consistent for pain at rest and on activity, and over time. We also explored the presence of category disagreements, defined as patients verbally describing no or mild pain scored above the mild/moderate cut point on the visual analogue scale, and patients verbally describing moderate or severe pain scored below the mild/moderate cut point on the visual analogue scale. Using 30 and 60 mm cut points, 1533 observations (12%) showed a category disagreement and using 35 and 80 mm cut points, 1632 (13%) showed a category disagreement. Around 1 in 8 simultaneous pain scores implausibly disagreed, possibly resulting in incorrect pain reporting. The reasons are not known but low rates of literacy and numeracy may be contributing factors. Understanding these disagreements between pain scales is important for pain research and medical practice.
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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 | 0.002 | 0.000 |
| 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; 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".