Clinical Study of Sentinel Lymph Node Detection Using Photodynamic Eye for Abdominal Radical Trachelectomy
Bibliographic record
Abstract
This study aimed to assess the accuracy of predicting pelvic lymph node status using sentinel lymph node (SLN) biopsy with indocyanine green (ICG) and to examine the outcomes of SLN biopsy-guided abdominal radical trachelectomy (ART). Patients with stage IA2-IB2 cervical cancer from January 2009 to January 2021 were included. ICG was injected before ART and SLNs were identified, excised, and assessed intraoperatively using fast-frozen sections. Systemic pelvic lymphadenectomy was subsequently performed. The SLN detection rate, sensitivity, and false-negative rate were determined. Thirty patients desiring fertility preservation were enrolled, of whom 26 successfully completed ART and four underwent radical hysterectomies because of metastatic primary SLNs. Bilateral SLNs were identified in all patients. The sensitivity, false-negative rate, and negative predictive value were 100%, 7.7%, and 92.3%, respectively. Three (12%) patients were lost to follow-up: two relapsed and one died of tumor progression. Of the nine patients who tried to conceive after surgery, four achieved pregnancy and three delivered healthy live infants. In women with early-stage cervical cancer who desired to conserve fertility, SLN mapping with ICG had a very high detection rate, sensitivity, and low false-negative rate. SLN biopsy-guided ART is a feasible and accurate method for assessing pelvic node status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".