S1354 Training Matters: How Trainee Skill Impacts Outcomes in Upper GI Bleeding—A Propensity-Matched US Study
Bibliographic record
Abstract
Introduction: Previously studies have mentioned teaching hospitals have worse outcomes compared to non-teaching hospitals for patients with UGIB. Hence, we aim to study the impact of trainee experience on patient care by examining outcomes of upper gastrointestinal (GI) bleeding in teaching hospitals. Methods: This study analyzed adult hospitalizations (age >18) with upper gastrointestinal bleeding (UGIB) in U.S. teaching hospitals using the National Inpatient Sample (2016–2020). Propensity score matching adjusted for baseline differences. Results: A total of 666,105 patients were admitted with the diagnosis of UGIB in US teaching hospitals during our study time frame. Among 336,490 patients were admitted during the first quarter (July, Aug and Sept) and 329,615 patients were admitted during the last quarter (Apr, May and June) of the academic year. Primary outcomes showed higher mortality rate for NVUGIB (2.46% vs 2.08%, P = 0.0020) during the last quarter of the academic year. It also had a higher incidence rate of ICU admission (4.54% vs 3.85%, P < 0.0001), CVC placement (1.64% vs 1.28%, P < 0.0001) and intubation (3.48% vs 3.04%, P = 0.0032). For VUGIB, there was no difference for outcomes in between 2 quarters. Conclusion: We found inpatient hospital mortality, ICU admissions, CVC placement and intubation rates were higher for NVUGIB during the last quarter of the academic year. Junior trainees getting more autonomy during later phase of their training might be responsible for these disparities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".