[Measurement of postoperative pain: analysis of the sensitivity of various self-evaluation instruments].
Bibliographic record
Abstract
OBJECTIVES: This study compared the sensitivity of two one-dimensional scales (a visual analog scale [VAS] and a verbal scale of pain intensity [VSPI]) and one multidimensional scale (McGill Pain Questionnaire-Spanish Version [MPQ-SV]) for detecting changes in pain after a variety of surgical procedures with postoperative analgesia provided by one of two methods. PATIENTS AND METHODS: Forty-two patients who underwent abdominal surgery, hysterectomy, cesarean, inguinal herniorrhaphy, subcostal or medial laparoscopic cholecystectomy were studied. Postoperative analgesia consisted of 1 mg/Kg of intravenous pethidine every 4 h in group one (n = 20) and intramuscular diclofenac every 12 h in group two. Assessment was at 24 h and/or at 48 and 72 h using the VAS, the VSPI and the MPQ-SV. RESULTS: All the scales were useful for assessing postoperative pain, giving estimates that were sensitive to variations in pain on days after the operation. The MPQ-SV was able to detect different pain-producing capacities for the surgical procedures more effectively than were the one-dimensional scales. The MPQ-SV was also able to discriminate the qualitative and quantitative differences among the mechanisms of action of opioid and nonsteroidal anti-inflammatory drugs, whereas the one-dimensional scales were unable to distinguish therapeutic approach. CONCLUSIONS: All the scales were sensitive to changes in postoperative pain, but the MPQ-SV gave more precise information of differences between analgesic treatments and among operations.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".