Intraoperative pathology consultation: error, cause and impact
Bibliographic record
Abstract
BACKGROUND: Correlation of intraoperative frozen section diagnosis with final diagnosis can be an important component of an institution's quality assurance process. METHODS: We performed a quality assurance review of 1207 frozen section diagnoses from 812 surgical cases performed in the Hamilton Regional Laboratory Medicine Programme during a 6-month period in 2007. We reviewed the frozen section and permanent slides from all potentially discordant cases using a multiheaded microscope to arrive at a consensus pertaining to the type and reason for error. We reviewed the clinical record to determine whether there had been a potential adverse impact on immediate clinical management. RESULTS: Frozen sections were most commonly requested for head and neck, nervous system and female genital tract specimens. Twenty-eight frozen sections (3%) were deferred. We identified 24 discordant diagnoses involving 3% of cases and 2% of specimens. The organ systems showing the greatest frequency of discordance relative to the total number from that system were the nervous system, head and neck, and the lungs. Of the errors identified, most occurred owing to diagnostic misinterpretation, followed by problems related to tissue sampling. There was a potential adverse impact on immediate clinical management in 14 cases. CONCLUSION: Our results add to the Canadian data on the correlation between frozen sections and permanent sections; we note comparability to the concordance rates reported in the literature.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".