Assessment of Latent Factors Contributing to Error: Addressing Surgical Pathology Error Wisely
Bibliographic record
Abstract
CONTEXT: Methods to improve surgical pathology patient safety include measuring the frequency of error in specific steps of the surgical pathology testing process, root cause analysis of active and latent components, and implementation of quality improvement initiatives. OBJECTIVE: To determine the frequency and cause of near-miss events in the specimen accessioning, setup, and biopsy-only gross examination testing steps of anatomic pathology. DESIGN: We used an observational checklist method to identify near-miss events. We performed root cause analysis to determine latent factors contributing to near-miss events. We conducted observations for 45 hours during 5 days, involving the accessioning and processing of 335 specimens. RESULTS: We detected a total of 2310 process-dependent and 266 operator-dependent near-miss events, resulting in a near-miss-event frequency of 5.5 per specimen. Root cause analysis showed that all process and operator near-miss events were associated with multiple system latent factors, including lack of standardized protocols, appropriate knowledge transfer, and focus on safety culture. CONCLUSION: We conclude that the increased focus on surgical pathology near-miss events will reveal latent factors that may be targeted for improvement.
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 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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".