Near-Infrared Fluorescence-Guided Segmentectomy: Added Benefit of Indocyanine Green Dye Diminishes With Surgeon Experience
Bibliographic record
Abstract
Background Near-infrared fluorescence (NIF)-mapping with indocyanine green dye (ICG) facilitates the identification of the intersegmental plane during minimally invasive segmentectomy. Our pilot study showed that ICG is associated with an increase in oncological margin distance from the tumour, greater than the surgeon’s best judgment. We hypothesized that, with greater experience, the surgeon’s judgement will improve, and the benefit of ICG will diminish. Methods This is a phase 2 single-arm trial of patients undergoing robotic-assisted segmentectomy for NSCLC tumours less than 3 cm. After isolating the diseased segment(s), the predicted intersegmental plane (Dp) was identified by the thoracic surgeon. After intravenous ICG injection, the true intersegmental plane (Dt) was revealed using NIF. The primary outcome was the average distance between Dt and Dp (Dt-Dp). Comparisons were performed across 3 temporal tertiles: tertile 1 (t1) comprised of the first 30 participants, and the remaining participants were divided equally for tertiles 2 (t2) and 3 (t3). Kruskal-Wallis test was used to compare differences between tertiles (α = 0.05). Results A total of 190 patients were enrolled from October 2016 to June 2021. The median age was 68 (interquartile range:62-72), and 57.37%(109/190) were women. ICG injection occurred in 60.53%(115/190) of the participants, and intersegmental plane visualization was achieved in 88.70%(102/115). Dt-Dp diminished significantly across tertiles: t1 = 20.65 ± 15.82 mm, t2 = 2.42 ± 15.49 mm, and t3 = 1.36 ± 9.87 mm ( P = 0.0001). Locally estimated scatterplot smoothing revealed that this distance approaches zero as the surgeon performs more cases. Conclusion In our single-surgeon experience with robotic-assisted segmentectomy for NSCLC, the added value of NIF-mapping with ICG diminishes with surgeon experience.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".