Validité de quatre échelles de douleur qui se sont révélées fiables par une revue systématique précédente : bref inventaire de la douleur, questionnaire court sur la douleur de McGill, dessin de la douleur, échelle visuelle de la douleur analogique : une revue systématique
Bibliographic record
Abstract
Pain is a multidimensional phenomenon that is subjective with physiological and psychological components. It is necessary to have valid and reliable pain measures. A previous study has identified Brief Pain Inventory (BPI), SF MPQ-1 and 2 (Short Form McGill Pain Questionnaire) as reliable. Visual Analog Pain Scale (VAS) and Pain drawing were less reliable but they are commonly used. The present study aimed to collect validity data of these four scales. Method : a Medline review via PubMed was carried out with no restriction on the publication date. Only English language papers were examined. Inclusion was conducted until June 2019. Data of validity (convergent, discriminant, construct, face and responsiveness) were extracted for each four scales. Results : 17 relevant articles were included for BPI, 8 for SF-MPQ-1, 5 for SF-MPQ-2, none for Pain drawing and 2 for VAS. Convergent validity was studied in several articles included for each scale. Construct validity was studied only for BPI and SF-MPQ. Discriminant validity was studied only for BPI in one article. Face validity was studied in one article for both SF-MPQ-1 and 2. Responsiveness was studied in one article for BPI, 2 articles for SF-MPQ-1 and 3 articles for SF-MPQ-2. Satisfactory validity data were found for BPI and SF-MPQ. Data were inadequate for Pain drawing and insufficient for VAS. Discussion : the five relevant validities were not all studied for BPI and SF-MPQ. Further studies could supplement these data. It will be necessary to study external validity of VAS, which is very useful in practice.
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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 | 0.081 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".