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 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 | 0.092 | 0.470 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".