Validation of the disease burden morbidity assessment by self-report in a French-speaking population
Bibliographic record
Abstract
BACKGROUND: The Disease Burden Morbidity Assessment (DBMA) is a self-report questionnaire used to estimate the disease burden experienced by patients. The aim of this study was to test and to measure the properties of the French translation of the DBMA (DBMA-Fv). METHODS: The original version of the DBMA was translated into French (Canadian) and first assessed during cognitive interviews. In the validation study, patients recruited during consecutive consultation periods completed the DBMA-Fv questionnaire while they were in the waiting room of a primary care setting (T1). Participants completed the same questionnaire mailed to their home two weeks later (T2). Concomitant validity of the DBMA-Fv was assessed using the Cumulative Illness Rating Scale (CIRS). Patient medical records were reviewed to verify chronic diseases and past medical history. RESULTS: Ninety-seven patients were recruited and 85 (88%) returned the mailed questionnaires; 5 (5.9%) were incomplete. DBMA-Fv scores of the 80 participants with a complete questionnaire at T2 ranged from 0 to 30 (median 5.5, mean 7.7, SD = 7.0). Test-retest reliability of the DBMA-Fv was high (ICC: 0.86, 95% CI: 0.79-0.92). The DBMA-Fv and the CIRS correlated moderately at T1 (r = 0.46, 95% CI: 0.26 - 0.62, p < 0.01) and T2 (r = 0.56, 95% CI: 0.38 - 0.70, p < 0.01). The mean (SD) sensitivity of patient reports of a condition in relation to chart review at T2 was 73.9 (8.4) (range 62.5% to 90%). The overall mean (SD) specificity was 92.2 (6.7) (range 77.6% to 98.6%). CONCLUSIONS: The DBMA-Fv's properties are similar to its English counterpart as to its median sensitivity and specificity compared to chart reviews. It correlated moderately with an established index of multimorbidity. A high percentage of patients were able to complete the test correctly as a mail questionnaire and it showed high test-retest reliability.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".