Medical laboratory associated errors: the 33-month experience of an on-line volunteer Canadian province wide error reporting system
Bibliographic record
Abstract
BACKGROUND: This article reports on the findings of 12,278 laboratory related safety events that were reported through the British Columbia Patient Safety & Learning System Incident Reporting System. METHODS: The reports were collected from 75 hospital-based laboratories over a 33-month period and represent approximately 4.9% of all incidents reported. RESULTS: Consistent with previous studies 76% of reported incidents occurred during the pre-analytic phase of the laboratory cycle, with twice as many associated with collection problems as with clerical problems. Eighteen percent of incidents occurred during the post-analytic reporting phase. The remaining 6% of reported incidents occurred during the actual analytic phase. Examination of the results suggests substantial under-reporting in both the post-analytic and analytic phases. Of the reported events, 95.9% were reported as being associated with little or no harm, but 0.44% (55 events) were reported as having severe consequences. CONCLUSIONS: It is concluded that jurisdictional reporting systems can provide valuable information, but more work needs to be done to encourage more complete reporting of events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".