Adverse Events Detected by Clinical Surveillance on an Obstetric Service
Bibliographic record
Abstract
OBJECTIVE: Adverse events are adverse patient outcomes resulting from medical care. We performed this study to estimate the rate of adverse events and potential adverse events-errors that have a high likelihood of causing patient harm-occurring during obstetric care. METHODS: This was a prospective cohort study of an obstetric unit in a teaching hospital. We included patients admitted consecutively to the hospital. A trained observer monitored patients for 72 triggers, which were predefined occurrences deemed likely to indicate an actual or potential adverse event. When a trigger occurred, the observer captured information describing it. A five-person multidisciplinary team, including the observer, three physicians, and a hospital risk manager, judged whether the trigger represented an adverse event or potential adverse event. Adverse events were further characterized as preventable. RESULTS: The cohort included 425 patients; 47% were in active labor. We identified 110 triggers. Nine were considered adverse events (risk 2%, 95% confidence interval [CI] 1-4%, rate 0.8 events per 100 patient days), and six were preventable (risk 1%, 95% CI 0-3%, rate 0.5 events per 100 patient days). The remaining triggers included 14 potential adverse events (risk 3%, 95% CI 2-5%, rate 1.3 events per 100 patient days). No adverse event resulted in permanent disability or death. Adverse events and potential adverse events were most commonly "system" problems, such as unavailable staff or operating rooms, or poor fetal outcomes, such as trauma to the newborn. CONCLUSION: Serious adverse events occur infrequently on an obstetric service. However, important quality problems are common and should be targeted for improvement. LEVEL OF EVIDENCE: II-2.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".