Pain descriptors and adaptation of Short Form McGill Pain Questionnaire 2 (SF-MPQ-2) for older people in Brunei Darussalam
Bibliographic record
Abstract
Background: Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage. It is common in older people, and tends to be under-reported and under-treated. In addition to quantifying pain severity using the visual analogue scale (VAS), use of the translated Short-Form McGill Pain Questionnaire (SF-MPQ-2) to identify pain descriptors may assist with pain assessment in older people. Aims: identify pain descriptors for different pain aetiology in older people using the adapted SF-MPQ-2 Brunei Malay version and compare pain severity assessments using the VAS and SF-MPQ-2. Patients and methods: A prospective study using the translated SF-MPQ-2 in older people admitted or seen in clinic under Orthopaedics and Geriatrics specialties between November 2018 and February 2019. Results: There were 75 participants, with 21 pain descriptors used. The main descriptors for fractures, osteoarthritis or muscle/tendon problems were identified. Despite pain medication, more than a third still experienced moderate to severe pain. However, almost all were satisfied with the pain management. There was a statistically significant difference between pain severity between the VAS and SF-MPQ-2, with the VAS possibly underestimating pain. Conclusions: The adapted SF-MPQ-2 appears feasible for use with older people in Brunei. Further studies are required to formally validate the SF-MPQ-2 in older patients and with specific medical conditions, such as diabetes or surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".