Comparative Analysis of Postoperative Complications of Sentinel Node Identification Using the SentiMag® Method and the Use of a Radiotracer in Patients with Breast Cancer
Bibliographic record
Abstract
(1) Background: The purpose of the study was a retrospective, comparative assessment of complications of the surgical sentinel node biopsy (SNB) procedure in breast cancer using the radiotracer method and the SentiMag® method on groups of patients after 3.5 years of use. (2) Methods: The material was a group of 345 patients with primary surgical breast cancer who underwent the SNB procedure with the use of a radiotracer in combination with wide local excision (WLE), simple amputation (SA) with SNB and an independent SNB procedure in the period from May 2018 to January 2021 in the Department of Oncological Surgery. Of the patients who were monitored in the Hospital Outpatient Clinic, 300 were enrolled. The analyzed group was compared in terms of the occurrence of the same complications with the group of 303 patients also operated on in our center in the period from January 2014 to September 2017, in which SN identification was performed using the SentiMag® method. (3) Results: The most common complications found were sensation disorders in the arm, which occurred in 16 (14.1%) patients using the radiotracer method, SentiMag®-11 (9.9%). By comparing the complication rate between the methods with the radiotracer (n = 300) and SentiMag® (n = 303), no significant differences were found. (4) Conclusions: Sentinel node (SN) identification using the radiotracer method and the SentiMag® method are comparable diagnostic methods in breast cancer, with a low risk of complications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".