Bibliographic record
Abstract
OBJECTIVE: To estimate underutilization of acute care settings in a tertiary care hospital. DESIGN: A retrospective and concurrent cohort study using chart reviews and the Intensity of service, Severity of illness, Discharge screen for Acute Care (ISD-AC(R)) tool to measure appropriateness of acute care for patients who were receiving care in a less acute setting, as an indicator of underutilization. SETTING: A 450-bed tertiary care teaching hospital. STUDY PARTICIPANTS: Patients discharged from the emergency department, patients discharged from acute care inpatient units and patients in acute, non-critical care settings. INTERVENTIONS: None. MAIN OUTCOME MEASURES: The percentage of patients discharged from the emergency department who did not meet the criteria for acute care discharge screens; the percentage of patients discharged from an acute care inpatient unit who did not meet the criteria for discharge screens; and the percentage of patients who were in acute, non-critical care beds and who met the criteria for critical care. RESULTS: It was found that six out of 168 patients [3.57%; 95% confidence interval (CI), 1.32-7.61%] did not meet the discharge screens at the time of discharge from the emergency department. Four out of 156 patients (2.56%; 95% CI, 0.70-6.43%) did not meet the discharge screens at the time of discharge from an acute care inpatient service and two out of 156 acute care patients (1.33%; 95% CI, 0.02-4.73%) who were in non-critical care beds met the criteria for critical care. CONCLUSION: These findings of underutilization may help to quantitate an unmet need in health care.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".