État des lieux des pratiques de prescription du bilan hépatique par les médecins généralistes : étude quantitative transversale descriptive menée par observation des médecins généralistes maîtres de stage en situation réelle de soin par les internes de phase socle, en 2022 en Aquitaine (Étude AquiBH)
Bibliographic record
Abstract
Introduction: liver blood tests are one of the most frequently prescribed laboratory tests in general medicine, three quarters of them are prescribed by GPs. There is no recent French study assessing the prescription of liver blood tests by GPs. The aim of the AquiBH study is to describe the prescribing procedure of liver blood tests by GPs in Aquitaine. Methods: we conducted a cross-sectional, prospective survey in a real-world setting in Aquitaine. Data collection was performed by medical interns during their level 1 outpatient internship in general medicine. They completed two sections of a data collection form: one when they observed their internship supervisor prescribing a liver blood test, and the other when they received the test results. Results: we analyzed 270 filled in forms. The main prescription indication was the exploration of a symptom (43%) then came the routine check-up (28,5%). We collected and analyzed 247 liver blood test results, nearly a quarter was abnormal (24,7%). The most common abnormality was hepatic cytolysis (39,3%). When liver tests were prescribed as part of a routine check-up, the frequency of discovery of an abnormality (17.8%) was comparable to that found in the exploration of a symptom (20.2%), but the abnormalities found were lower and required less treatment. Conclusion: liver tests are a tool widely used by GPs in diagnostic, screening and follow-up procedures. It is widely performed and represents a significant cost, but there is no recommendation for its prescription. It would be interesting to set up guidelines for the prescription of liver tests in general practice to optimize their use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".