Development and validation of a brief, descriptive Danish pain questionnaire (BDDPQ)
Bibliographic record
Abstract
BACKGROUND: A new pain questionnaire should be simple, be documented to have discriminative function, and be related to previously used questionnaires. METHODS: Word meaning was validated by using bilingual Danish medical students and asking them to translate words taken from the Danish version of the McGill pain questionnaire into English. Evaluative word value was estimated using a visual analog scale (VAS). Discriminative function was assessed by having patients with one of six painful conditions (postherpetic neuralgia, phantom limb pain, rheumatoid arthritis, ankle fracture, appendicitis, or labor pain) complete the questionnaire. RESULTS: We were not able to find Danish words that were reliably back-translated to the English words 'splitting' or 'gnawing'. A simple three-word set of evaluative terms had good separation when rated on a VAS scale ('let' 17.5+/-6.5 mm; 'moderat' 42.7+/-8.6 mm; and 'staerk' 74.9+/-9.7 mm). The questionnaire was able to discriminate among the six painful conditions with 77% accuracy by just using the descriptive words. The accuracy of the questionnaire increased to 96% with the addition of evaluative terms (for pain at rest and with activity), chronicity (acute vs. chronic), and location of the pain. CONCLUSIONS: A Danish pain questionnaire that subjects and patients can self-administer has been developed and validated relative to the words used in the English McGill Pain questionnaire. The discriminative ability of the questionnaire among some common painful conditions has been tested and documented. The questionnaire may be of use in patient care and research.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".