Thoracoscopic left upper division (S1/2/3) resection: alternative posterior approach
Bibliographic record
Abstract
A minimally invasive pulmonary segmentectomy allows adequate oncological treatment in selected cases while preserving lung parenchyma and minimizing perioperative morbidity and length of hospital stay. Most lung segments may be resected as segmentectomies or as part of bisegmentectomies (as is the case for the lingula). In the author's experience, left upper division resection (S1, S2, S3 trisegmentectomy) may be challenging. Because the lingula and lingular structures need to be preserved, they may obstruct visualization and hamper the movement of the dissecting instruments. This has been the author's experience using an anterior approach. In contradistinction, a posterior approach allows direct access to the artery and arterial branches and greatly facilitates access to the segmental bronchus. Dissection of the bronchus proceeds from back to front, away from the artery. In addition, when we are isolating and encircling the bronchus, we have already freed the artery from the bronchus and it is safely out of the way. The advantages of a posterior approach are particularly apparent when pathological nodes between the bronchus and artery make the dissection tedious, as in the case presented. Regardless of the surgical approach, S1/S2/S3 trisegmentectomy remains a challenging procedure that requires great care in its execution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".