Abstract 21: Are Stroke Outcomes Influenced by New Starting Residents? The July Effect: Myth or Reality?
Bibliographic record
Abstract
Background: Acute stroke care provided by comprehensive stroke centers usually follows prespecified protocols. However, there are concerns about lower quality of care and poorer stroke outcomes early after new trainnees (e.g.) residents start in July in academic/teaching hospitals. This has been called ‘the July effect’. Objective: To evaluate access to specialized care and outcomes among patients admitted with an acute ischemic stroke (AIS) in July and other months. Hypothesis: We hypothesized that there were no significant differences in access to stroke care and outcomes for patients admitted in July when new trainees start at academic centers. Methods: Patients presenting with an AIS at 11 stroke centers in Ontario, Canada, between 2003 and 2009 were identified from the Registry of the Canadian Stroke Network. We compared performance measures and functional outcomes (death at 30 days, modified Rankin Scale 3 to 5 at discharge) between AIS patients admitted in July of each studied year and those who admitted during other months. Results: Of 10,319 eligible patients with an AIS, 882 (8.5%) were admitted in July. There was not difference in age, sex, or baseline stroke severity between patients admitted in July or other months. Among the performance measures analyzed, AIS admitted in July were less likely to receive thrombolysis (12.1% vs. 16.0%, p=0.002), swallowing test (64.4% vs. 67.9%, p=0.033), and admission to stroke unit (61.9% vs. 67.6%, <0.001). There was no difference in death at 30-days (16.4% vs. 16.1%, p=0.823) or poor functional outcome (61.0% vs. 63.5%, p=0.14) between two groups (Table). Conclusion: AIS patients admitted in July were less likely to receive thrombolysis and be admitted to stroke units compared to patients admitted on the rest of the year. However, there was no negative effect of “admission on July” on functional outcome or death.
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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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".