Device for sectioning prostatectomy specimens to facilitate comparison between histology and in vivo MRI
Bibliographic record
Abstract
PURPOSE: To develop a device for sectioning prostatectomy specimens that would facilitate comparison between histology and in vivo MRI. MATERIALS AND METHODS: A multi-bladed cutting device was developed, which consists of an adjustable box capable of accommodating a prostatectomy specimen up to 85 mm in size in the lateral direction, a "plunger" tool to press on the excised gland from the top to prevent it from rolling or sliding during sectioning, and a multi-bladed knife assembly capable of holding up to 21 blades at 4-mm intervals. The device was tested on a formalin fixed piece of meat and subsequently used to section a prostatectomy specimen. Histology sections were compared with T2-weighted MR images acquired in vivo before the prostatectomy procedure. RESULTS: The prostatectomy specimen slices were very uniform in thickness with each face parallel to the other with no visible sawing marks on the sections by the blades after the cut. MRI and histology comparison showed good correspondence between the two images. CONCLUSION: The developed device allows sectioning of prostatectomy specimens into parallel cuts at a specific orientation and fixed intervals. Such a device is useful in facilitating accurate correlation between histology and MRI data.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".