Description of patients with eating disorders by general practitioners: a cohort study and focus on co-management with depression
Bibliographic record
Abstract
BACKGROUND: International guidelines often state that general practitioners (GPs) provide early management for most patients with eating disorders (EDs). GP management of EDs has not been studied in France. Depressive disorders are often a comorbidity of EDs. The aims of this study were to describe in France the characteristics of people with all subcategories of EDs (Anorexia Nervosa, Bulimia Nervosa, ED Not Otherwise Specified) managed by their GPs and to study the management temporality between depression and all subcategories of EDs. METHODS: Retrospective cohort study of patients with EDs visiting French GPs. Data collected from 1994 through 2009 were extracted from the French society of general electronic health record. A descriptive analysis of the population focused on depression, medication such as antidepressants and anxiolytics, and the management temporality between depression and EDs. RESULTS: 1310 patients aged 8 years or older were seen at least once for an ED by a GP participating in the database out of 355,848 patients, with a prevalence rate of 0.3%. They had a mean age of 35.19 years, 82.67% were women. 41.6% had anorexia nervosa, 26.4% bulimia nervosa, and 32% an ED not otherwise specified. Overall, 32.3% had been managed at least once for depression, and 18.4% had been prescribed an antidepressant of any type at least once. Benzodiazepines had been prescribed at least once for 73.9% of the patients treated for depression. Patients with an ED seen regularly by their GP ("during" profile) received care for depression more frequently than those with other profiles. 60.9% had a single visit with the participating GP for their ED Treatment and management for depression did not precede care for EDs. CONCLUSIONS: Data extracted from the French society of general practice were the only one available in France in primary care about EDs and our study was the only one on this topic. The frequency of visits for EDs was very low in our general practice-based sample. Depressive disorders were a frequent comorbidity of EDs. GPs could manage common early signs of depression and EDs, especially if they improved their communication skills and developed collaborative professional management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".