Disclosure of Diagnostic Errors: the Death Knell of Retrospective Pathology Reviews?
Bibliographic record
Abstract
In Brief Full disclosure of medical errors to patients is now widely seen as an essential component of error management, although its update into daily clinical practice is variable. Laboratory diagnostic errors are discovered in retrospective reviews of previous surgical and cytopathology cases. This quality assurance practice is a valuable tool of practice audit and change for both cytotechnologists and pathologists. Presently, these diagnostic errors are only reported to the clinician and patient if the new finding affects current patient management. Mandatory full disclosure of all diagnostic errors discovered in the retrospective review process would have a significant adverse impact on cytotechnologists, pathologists, the laboratory, the clinic, the institution, and insurers. Retrospective pathology review would become so burdensome that its survival would be in jeopardy-unless measures are undertaken to ameliorate the anticipated adverse consequences. Any policy of full disclosure of diagnostic pathology errors to patients will adversely impact retrospective pathology review and quality assurance programs-unless it is implemented with prudent safeguards.
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 | 0.002 | 0.026 |
| 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 | 0.000 | 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 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".