Surgical technique: lung-sparing sleeve bronchoplasty
Bibliographic record
Abstract
Abstract: Total lung-parenchyma-sparing bronchoplasty is a step forward in the treatment of low-grade malignant tumors and benign endobronchial lesions because it allows resection while preserving lung capacity. This technique is feasible in several of the bronchial segments, including the right main bronchus, the intermedius bronchus, and the left main bronchus. Conventionally bronchoplasty has been done using open surgery with a thoracotomy, but lung-parenchyma-sparing bronchoplasty can now be performed minimally invasively by experienced surgeons through a single incision, resulting in less pain, fewer complications, a faster recovery, and equivalent oncological results. Low-grade, localized tumors and benign strictures are the ideal pathologies for lung-sparing bronchoplasty. Keys factors for optimal results include proper patient selection, a thorough oncological and functional evaluation, and meticulous surgical technique to attain negative margins and a complete resection. Reports on uniportal lung-parenchyma-sparing bronchoplasty, although scarce due to the nature and complexity of the procedure, have been published confirming the feasibility and safety of the procedure when performed by well-trained surgeons. Here, we present a lung-parenchyma-sparing sleeve resection and anastomosis using a uniportal approach through a single 3–4 cm incision in the 5th intercostal space to treat a typical carcinoid tumor (a low-grade neuroendocrine tumor) in the left main bronchus.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".