Laboratory error reporting rates can change significantly with year-over-year examination
Bibliographic record
Abstract
BACKGROUND: Incident reporting systems are useful tools to raise awareness of patient safety issues associated with healthcare error, including errors associated with the medical laboratory. METHODS: Previously, we presented the analysis of data compiled by the British Columbia Patient Safety & Learning System over a 3-year period. A second comparable set was collected and analyzed to determine if reported error rates would tend to remain stable or change. RESULTS: Compared to the original set, the second set presented changes that were both materially and statistically significant. Overall, the total number of reports increased by 297% with substantial changes between the pre-examination, examination and post-examination phases (χ2: 993.925, DF=20; p<0.00001). While the rate of change for pre-examination (clerical and collection) errors were not significantly different than the total year results, the rate of change for reporting examination errors rose by 998%. While the exact reason for dramatic change is not clear, possible explanations are provided. CONCLUSIONS: Longitudinal error rate tracking is a useful approach to monitor for laboratory quality improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".