A New Approach to Breast Specimen Orientation: Avoiding Pitfalls with the Specimen Plate Concept
Bibliographic record
Abstract
Accurate specimen marking is crucial during breast cancer surgery to avoid misorientation, which can lead to inadequate re-excision and tumor recurrence. We studied the marking methods at various breast cancer centers to create a tool that would prevent specimen misorientation. An online questionnaire was used to survey marking procedures at major breast cancer centers in Hungary, and a tool was developed using a troubleshooting method. Twelve out of twenty units responded (60%). Nine use an institutionally standardized marking system. Less than half of the surgical teams found specimen mammograms to be unambiguous. In more than 70% of departments, pathologists were uncertain about breast specimen orientation. Ambiguous marking methods caused orientation errors in half of the cases, while unclear marking directions caused the rest. Most pathologists (85%) and surgeons (75%) believed that coronal plane specimen mammography would help solve the problem. A plastic specimen plate has been developed to anchor breast tissue to a coronal breast scheme as seen in mammography images, providing clear localization information throughout the surgical process. There is a lack of standardization in breast specimen orientation and marking in Hungary. An optimized orientation toolkit is being developed to ensure consistent interpretation of specimen mammograms by surgeons and pathologists.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".