Diagnostic Discrepancies in Mandatory Slide Review of Extradepartmental Head and Neck Cases: Experience at a Large Academic Center
Bibliographic record
Abstract
CONTEXT: Medical error is a significant problem in the United States, and pathologic diagnoses are a significant source of errors. Prior studies have shown that second-opinion pathology review results in clinically major diagnosis changes in approximately 0.6% to 5.8% of patients. The few studies specifically on head and neck pathology have suggested rates of changed diagnoses that are even higher. Objectives .- To evaluate the diagnostic discrepancy rates in patients referred to our institution, where all such cases are reviewed by a head and neck subspecialty service, and to identify specific areas with more susceptibility to errors. DESIGN: Five hundred consecutive, scanned head and neck pathology reports from patients referred to our institution were compared for discrepancies between the outside and in-house diagnoses. Major discrepancies were defined as those resulting in a significant change in patient clinical management and/or prognosis. RESULTS: Major discrepancies occurred in 20 cases (4% overall). Informative follow-up material was available on 11 of the 20 patients (55.0%), among whom, the second opinion was supported in 11 of 11 cases (100%). Dysplasia versus invasive squamous cell carcinoma was the most common (7 of 20; 35%) area of discrepancy, and by anatomic subsite, the sinonasal tract (4 of 21; 19.0%) had the highest rate of discrepant diagnoses. Of the major discrepant diagnoses, 12 (12 of 20; 60%) involved a change from benign to malignant, one a change from malignant to benign (1 of 20; 5%), and 6 involved tumor classification (6 of 20; 30%). CONCLUSIONS: Head and neck pathology is a relatively high-risk area, prone to erroneous diagnoses in a small fraction of patients. This study supports the importance of second-opinion review by subspecialized pathologists for the best care of patients.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".