Higher diagnostic accuracy with the ThinPrep method in a simulated intraoperative environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it