Influence of house‐staff experience on teaching‐hospital mortality: The “July Phenomenon” revisited
Bibliographic record
Abstract
BACKGROUND: The "July phenomenon" refers to a purported worsening of outcomes in teaching-hospital patients with the arrival of new, inexperienced house-staff. Previous quantitative studies of new house-staff and increased mortality have been limited primarily by a focused patient population and the use of limited data to adjust for severity of patient illness. METHODS: We included all medicine, surgical, and obstetrical patients admitted to a teaching hospital in Ontario, Canada between April 15, 2004 and December 31, 2008. We calculated the ratio of observed to expected weekly number of deaths in hospital. The expected number of deaths was calculated using a validated, discriminative, and well-calibrated multivariate survival model. Collective house-staff experience was modeled from a minimum on July 1st to a maximum on June 30th using five distinct patterns. RESULTS: We studied 259,748 encounters that included 164,318 people. The mortality rate was 3.0%. The ratio of observed to expected number of weekly deaths was not associated with collective house-staff experience, irrespective of the pattern in which it was modeled. The lack of association between risk of death in hospital and house-staff experience did not vary by admission type (urgent vs elective) or specialty (medicine vs surgery). CONCLUSION: At our hospital, we found no association between the arrival of new house-staff and the adjusted risk of death in hospital. These data, along with the results of the vast majority of previous studies in this field, make the existence of the "July Phenomenon" for inpatient mortality extremely unlikely.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".