Touch imprint cytology is useful for the intraoperative pathological diagnosis of PitNETs’ surgical margins
Bibliographic record
Abstract
Touch imprint cytology (TIC) and frozen section (FS) procedures are essential for intraoperative pathological diagnosis (IPD). They are invaluable tools for therapeutic decision-making, helping surgeons avoid under or overtreatment of patients. Pituitary neuroendocrine tumors (PitNETs) are generally small, slow-growing tumors with low-grade malignancy located at the base of the skull where it is impossible to maintain a wide tumor margin. Therefore, transsphenoidal surgery (TSS) should be performed with necessary caution, and with sufficient and minimal resection. Thus, this study aimed to evaluate the diagnostic accuracy of TIC for the diagnosis of PitNET and determine its ability to accurately evaluate the surgical margin compared to the FS procedure. A total of 104 fresh specimens from 28 patients who underwent TSS for PitNETs were examined using TIC and FS. TIC specimens were categorized according to the cell imprinting pattern. All specimens with a large number of neuroendocrine cells diffusely attached to the glass surfaces had PitNET components. Contrarily, no rich or diffuse cell attachments were observed in any non-tumoral endocrine cells. In conclusion, recognizing a pattern of endocrine cell adherence to glass is highly effective in IPD to certify the existence of a PitNET component.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".