Benchmarking the burden of 100 diseases: results of a nationwide representative survey within general practices
Bibliographic record
Abstract
Objective To assess the burden of diseases and quality of life (QOL) of patients for a large variety of diseases within general practice. Design In a representative nationwide cross-sectional study, a total of 825 general practitioners (GPs) were randomly selected from across France. Independent investigators recruited 8559 patients attending the GPs' practices. Data on QOL (12-Item Short Form questionnaire) and other individual characteristics were documented by the independent investigators for all participants in the waiting room. Medical information was recorded by GPs. Sampling was calibrated to national standards using the CALMAR (CALage sur MARges) weighting procedure. Associations of lower scores (ie, below vs above the first quartile) of physical and mental component scores (physical component summary score (PCS) and mental component summary score (MCS), respectively) with main diseases and patients characteristics were estimated using multivariate logistic regression. Weighted morbidity rates, PCS and MCS were computed for 100 diagnoses using the International Classification of Diseases (9th version). Results Overall mental impairment was observed among patients in primary care with an average MCS of 41.5 (SD 8.6), ranging from 33.0 for depressive disorders to 45.3 for patients exhibiting fractures or sprains. Musculoskeletal diseases were found to have the most pronounced effect on impaired physical health (OR=2.31; 95% CI 2.08 to 2.57) with the lowest PCS (45.6 (SD 8.8)) and ranked first (29.0%) among main diagnoses experienced by patients followed by cardiovascular diseases (26.7%) and psychological disorders (22.0%). When combining both prevalence and QOL, musculoskeletal diseases represented the heaviest burden in general practice. Conclusions Etude épidémiologique de l'Impact de santé public sur 3 groupes de pathologies (EPI3) is the first study to provide reference figures for burden of disease in general practice across a wide range of morbidities, particularly valuable for health-economics and healthcare-system evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".