Higher diagnostic accuracy with the ThinPrep method in a simulated intraoperative environment
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of intraoperative fine needle aspiration cytology samples prepared by the ThinPrep method to conventional cytological methods. Specimen adequacy and turn around time (TAT) were also assessed. METHODS: Fifty consecutive fresh tumours submitted for histological analysis were aspirated and each prepared as follows: (i) direct smear with H&E stain, (ii) direct smear with Pap stain, (iii) ThinPrep slide with H&E stain, and (iv) ThinPrep slide with Pap stain. The slides were randomly distributed to three cytopathologists for interpretation. The quality of the preparation, the diagnosis and the time needed for interpretation were recorded. RESULTS: Accuracy was measured as the percentage of absolute agreement between the cytological and the histopathological diagnoses of the lesions. Histologically, there were 43 malignant and six benign lesions and one atypical lipoma. The TAT began when the slides/cytolyte specimens arrived at the lab and ended with the pathologist's diagnosis. CONCLUSIONS: In terms of accuracy and specimen adequacy, ThinPrep slides with Pap stain is the best procedure. This advantage however is offset by the longer testing time.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".