Mammary ductoscopy in the evaluation and treatment of pathologic nipple discharge: a Canadian experience.
Bibliographic record
Abstract
BACKGROUND: Mammary ductoscopy allows direct visualization of ductal epithelium using a fibreoptic microendoscope. As the first centre in Canada to apply ductoscopy to surgical practice, we report our experience with this technology. METHODS: Between 2004 and 2008, 65 women with pathologic nipple discharge underwent ductoscopy before surgical duct excision under general anesthetic. Prospective data collection included cannulation and complication rates, procedure length and lesion visualization rate compared with preoperative ductography, if performed. In addition, we classified the endoscopic appearance according to Makita and colleagues and correlated it with surgical pathology. RESULTS: It took longer than 6 months to overcome technical problems before the routine use of ductoscopy in the operating room. The ductoscope was easy to use: we achieved cannulation in 63 of 66 breast ducts (95%) and we visualized a lesion in 52 of 63 breast ducts (83%). The mean procedure length was 5.1 minutes, with no complications. Lesions seen on ductography were seen endoscopically 30 of 33 (91%) times. All 3 malignancies were seen: invasive carcinoma in 1 of 62 (1.6%) and in situ disease in 2 of 62 (3.2%) patients. Surgeons found ductoscopy helpful in defining the extent of duct excision. Except for the "polypoid solitary" class, which accurately predicted a papilloma (23/23), we found poor correlation between Makita and colleague's endoscopic classification and final pathology. CONCLUSION: Ductoscopy is feasible, safe and practical. Our surgeons routinely use it to identify the location and extent of duct excision without ordering preoperative ductography. Identifying pathology based on the endoscopic appearance is unreliable unless the lesion is solitary and polypoid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".