58 High Unsatisfactory Rates of Thyroid Fine Needle Aspirations—Are Pathologists Part of the Problem?
Bibliographic record
Abstract
It has been identified that the nondiagnostic/unsatisfactory rate (ND/UNS) of thyroid fine needle aspirations (FNAs) is dependent on the operators who provide the samples; however, little is published on whether the diagnostic interpretation of the pathologists contributes to the problem of suboptimal thyroid samples. A total of 679 thyroid FNAs sent to St. Boniface Hospital in Winnipeg, Canada, were randomly selected and reviewed retrospectively. Cases read by pathologists who had interpreted <20 cases were excluded, resulting in 628 cases randomly assigned to 12 pathologists. The final cytologic diagnoses were recorded, and the overall ND/UNS was determined for the group as well as for each individual pathologist. Of the 628 cases, 145 were signed out by the pathologist as ND/UNS (23%). The average number of FNAs per pathologist was 52, with the individual numbers ranging from 22 to 103 FNAs per pathologist. The individual ND/UNS for each pathologist ranged from 14% to 34%, the deviation from the group average of 23% ranged from –9% to +11%. Six pathologist ND/UNS rates were higher than the group average, five were lower, and one pathologist’s ND/UNS rate corresponded to the group average. The ND/UNS rates demonstrated individual differences by each pathologist; however, the distribution of the individual rates was relatively symmetric about the group average. Even the lowest ND/UNS rate was higher than what most studies have published by different groups. We conclude that pathologists may yield individual differences in applying criteria for nondiagnostic/unsatisfactory FNAs. Low cellularity of the incoming samples, however, is an area needing to be addressed (perhaps best by rapid on-site assessment) to decrease overall ND/UNS rates and improve patient care.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".