A Comparison of Digitized Frozen Section and Smear Preparations for Intraoperative Neurotelepathology
Bibliographic record
Abstract
Background: Intraoperative consultations in neuropathology are often assessed by smear preparations rather than by frozen sections. Both techniques are standard practice for light microscopic examination on site, but there is little data comparing these techniques in a telepathology setting. Methods: Thirty cases of brain tumours submitted for intraoperative consultation at our institution between July and December 2010 were identified in which both frozen section and tissue smear preparations were available for digitization at 20× magnification. Slides were digitized using a Hamamatsu Nanozoomer 2.0 HT whole slide scanner, and resulting digital images were visualized at 1680 × 1050 pixel resolution with NDP. view software. Results: The original intraoperative diagnosis was concordant with the sign out diagnosis in 29/30 cases; one tumeur was initially interpreted as a high grade glioma but proved to be a lymphoma at sign out. Digitized frozen section slides were sufficient for diagnosis at 10× magnification in 27/30 cases. Digitized tissue smears were sufficient for diagnosis at 10× magnification in 28/30 cases. In two cases tumour was present on the tissue smear but not the frozen section (one case of recurrent astrocytoma, one case of meningeal carcinomatosis). In one case of lymphoma, tumour was present on frozen section only. These discrepancies were attributed to tissue sampling rather than image quality. Examination of digitized slides at higher magnfication (20×) permitted confirmation of mitoses and Rosenthal fibers on tissue smear preparations, but did not change the primary diagnosis. Intra-slide variations in tissue thickness on smear preparations led to variable loss of focus in digitized images, but did not affect image quality in thinner areas of the smear or impede diagnosis. Conclusion: Digitized tissue smears are suitable for intraoperative neurotelepathology and provide comparable information to digitized frozen sections at medium power magnification.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".