Improving the organization of consultation departments in university hospitals
Bibliographic record
Abstract
RATIONALE: Changes in the demography of doctors require changes in care practices. OBJECTIVES: The aim of this study was to identify factors associated with doctors' workload in the ophthalmology consultation department of a university hospital, with a view to developing methods to improve the organization of hospital outpatient clinics. METHODS: A 10-day cross-sectional survey was carried out in an ophthalmology outpatient clinic (in- and outpatient consultations, including emergencies) specializing in the uveitis care. Demographic and management data for each patient were collected on a structured form. The doctor's workload was assessed, using a scale taking into account the duration of the consultation and the number of diagnostic tests performed, as a function of management complexity. RESULTS: Of the 861 consultations studied, 39.7% were highly complex. The level of complexity of consultations was correlated with the type of referral (phi = 0.602), consultation duration (phi = 0.545), the number of consultations in the previous year (phi = 0.499), and the number of diagnostic tests performed (phi = 0.445). Consultations were longer and diagnostic tests were more frequently performed if patients had been referred by an ophthalmologist, consulted a faculty doctor or a fellow, or presented with uveitis. Consultations were also more complex for patients with at least four previous consultations in the past year. CONCLUSIONS: Type of referral, status of the attending doctor and number of consultations within the course of 1 year were associated with doctors' workload and could be taken into account to predict the duration of complexity of consultations when scheduling appointments.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".