Bibliographic record
Abstract
OBJECTIVE: To determine if operation of an outpatient "after-hours" clinic (AHC) was associated with a reduction in local emergency department (ED) visits. STUDY SETTING: Leduc, Alberta, Canada is a city of approximately 20 000 people. There is one hospital ED and a single AHC. Information on AHC and ED visits was collected from January 2005 to February 2008. STUDY DESIGN: This was an observational before-and-after study of monthly ED visit frequency, stratified by patient illness severity. DATA COLLECTION: We collected patient visits per month to the ED before and after AHC implementation. Twenty-eight months of ED patient visit information were collected (14 months of pre-AHC; 14 months of post-AHC). A Wilcoxon signed-rank test was used to test the statistical strength of difference in ED visits, matched by month, before and after the AHC became operational. RESULTS: An average of 261.2 (standard deviation [SD], 47.7) patient visits per month were made to the AHC. There was a mean reduction of 36.7 (standard error of mean [SEM], 9.6; P = .009) total patient visits per month and 49.3 (SEM, 5.6; P = .001) fewer semiurgent patient visits per month to the Leduc ED during AHC operating hours. CONCLUSIONS: There was a consistently observable and statistically significant reduction in total patients visiting the ED subsequent to AHC operation. Stratified analysis indicated that this was due to fewer semiurgent patients seeking medical care at the local ED.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".