A reduction in errors is associated with prospectively recording them
Bibliographic record
Abstract
OBJECT: Error recording and monitoring is an important component of error prevention and quality assurance in the health sector given the huge impact of medical errors on the well-being of patients and the financial loss incurred by health institutions. With this in mind, assessing the effect of reporting errors should be a cause worth pursuing. The object in this study was to examine the null hypothesis that recording and publishing errors do not affect error patterns in a clinical practice. METHODS: Intraoperative errors and their characteristics were prospectively recorded between May 2000 and May 2013 in the neurosurgical practice of the senior author (M.B.). The error pattern observed between May 2000 and August 2006, which has been previously described (Group A), was compared with the error pattern observed between September 2006 and May 2013 (Group B). RESULTS: A total of 1108 cases in Group A and 974 cases in Group B were surgically treated. A total of 2684 errors were recorded in Group A, while 1892 errors were recorded in Group B. The ratios of cranial to spinal procedures performed in Groups A and B were 3:1 and 10:1, respectively, while the ratios of general to local anesthesia in the two groups were 2:1 and 1.3:1, respectively (p < 0.0001 for both). There was a significantly decreased proportion of cases with error (87% to 83%, p < 0.006), mean errors per case (2.4 to 1.9, p < 0.0001), proportion of error-related complications (16.7% to 5.5%, p < 0.002), and clinical impacts of error (2.7% to 1.0%, p < 0.0001) in Group B compared with Group A. Errors in Group B tended to be more preventable than those in Group A (85.8% vs 78.5%, p < 0.0001). A significant reduction was also noticed with most types of error. A descending trend in the mean errors per case was demonstrated from the years 2001 to 2012; however, an increased severity of errors (22.6% to 29.5%, p < 0.0001) was recorded in Group B compared with Group A. CONCLUSIONS: Data in this study showed that the act of recording errors might alter behaviors, resulting in fewer errors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".