The measurement of postoperative pain: A comparison of intensity scales in younger and older surgical patients
Bibliographic record
Abstract
The psychometric properties of pain intensity scales for the assessment of postoperative pain across the adult lifespan have not been reported. The objective of this study was to compare the feasibility and validity of the Numeric Rating Scale (NRS), Verbal Descriptor Scale (VDS), and Visual Analog Scale (horizontal (VAS-H) and vertical (VAS-V) line orientation) for the assessment of pain intensity in younger and older surgical patients. At 24h following surgery, 504 patients, who were receiving i.v. morphine via patient-controlled analgesia, completed the pain intensity measures and the McGill Pain Questionnaire (MPQ) in a randomized order. They were asked which scale was easiest to complete, the most accurate measure, and which they would most prefer to complete in the future, as an index of face validity. The amount of opioid self-administered was recorded. Age differences in postoperative pain intensity were not found. However, elderly patients obtained lower MPQ scores and self-administered less morphine than younger people. Psychometric analyses suggested that the NRS was the preferred pain intensity scale. It had low error rates, and higher face, convergent, divergent and criterion validity than the other scales. Most importantly, its properties were not age-related. The VDS also had a favourable profile with low error rates and good face, convergent and criterion validity. Finally, difficulties with VAS use among the elderly were identified, including high rates of unscorable data and low face validity. Its use with elderly postoperative patients should be discouraged.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".