Quality and patient safety in the diagnosis of breast cancer
Bibliographic record
Abstract
The media, medical legal, and safety science perspectives of a laboratory medical error differ and assign variable levels of responsibility on individuals and systems. We examine how the media identifies, communicates, and interprets information related to anatomic pathology breast diagnostic errors compared to groups using a safety science Lean-based quality improvement perspective. The media approach focuses on the outcome of error from the patient perspective and some errors have catastrophic consequences. The medical safety science perspective does not ignore the importance of patient outcome, but focuses on causes including the active events and latent factors that contribute to the error. Lean improvement methods deconstruct work into individual steps consisting of tasks, communications, and flow in order to understand the affect of system design on current state levels of quality. In the Lean model, system redesign to reduce errors depends on front-line staff knowledge and engagement to change the components of active work to develop best practices. In addition, Lean improvement methods require organizational and environmental alignment with the front-line change in order to improve the latent conditions affecting components such as regulation, education, and safety culture. Although we examine instances of laboratory error for a specific test in surgical pathology, the same model of change applies to all areas of the laboratory.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".