Safety on an inpatient pediatric otolaryngology service: Many small errors, few adverse events
Bibliographic record
Abstract
OBJECTIVES: Studies of medical error demonstrate that errors and adverse events (AEs) are common in hospitals. There are little data of errors on pediatric surgical services. METHODS: We retrospectively reviewed 50 randomly selected inpatient admissions to the otolaryngology service at a tertiary care children's hospital. We used a "zero-defect" paradigm, recording any error or adverse event-from minor errors such as illegible notes to more significant errors such as mismanagement resulting in a bleeding emergency. RESULTS: A total of 553 errors/AEs were identified in 50 admissions. Most (449) were charting or record-keeping deficiencies. Minor AEs (n = 26) and moderate AEs (n = 8) were present in 38% of admissions; there were no major AEs or permanent morbidity. Medication-related errors occurred in 22% of admissions, but only two resulted in minor AEs. There was a positive correlation between minor errors and AEs; however, this was not statistically significant. CONCLUSIONS: Multiple errors occurred in every inpatient pediatric otolaryngology admission; however, only 26 minor and eight moderate AEs were identified. The rate of errors per 1,000 hospital days (6,356 per 1,000 days) is higher than previously reported in voluntary reporting studies, possibly due to our methodology of physician review with a "zero-defect" standard. Trends in the data suggest that the presence of small errors may be associated with the risk of adverse events. Although labor-intensive, physician chart review is a valuable tool for identifying areas for improvement. Although small errors were common, there were few harms and no major morbidity.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".