Consultation patterns and clinical correlates of consultation in a tertiary care setting.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Consultation in hospital is an essential tool for acquiring subspecialty support when managing patients. There is limited knowledge on the utilization of subspecialty consultation from hospital based general internists. Consultation patterns to medical subspecialists and the patient factors that may influence consultation are reported for general medical services. METHODS AND FINDINGS: Hospital discharge data were obtained for patients from medical services over a 2-year period. Consultations requested to medicine subspecialties were identified, and then reported by type and frequency. Information on demographic factors, clinical diagnoses, length of stay (LOS), time in critical care units, and disposition were compared for patients with and without consultation.3979 patients were hospitalized during the study and 2885 consultations occurred. Almost half of the patients received at least one consultation (48.3%). Gastroenterology (26.3%), infectious diseases (14.6%) and respirology (13.6%) were the most frequently consulted services. Patients with consultation had a greater number of total diagnoses (7.3 vs. 5.5, P < 0.001), a greater mean LOS (15.9 vs. 6.8 days), were more likely to spend time in the ICU (11.5% vs. 3.5%) and CCU (4.3% vs. 1.2%), and to expire in hospital (10.7% vs. 4.9%). CONCLUSION: Consultation occurs frequently and its presence is an indicator of patient complexity and high use of health system resources. Analysis of consultation patterns for specific patient populations could assist in optimizing efficiency in health care delivery. Targeting quality improvement strategies toward optimizing consultation processes, engaging heavily utilized subspecialties in educational roles and assisting with resource planning are areas for future consideration.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it