CT-guided microcoil VATS resection of lung nodules: a single-centre experience and review of the literature
Bibliographic record
Abstract
BACKGROUND: Video-assisted thoracoscopic surgery (VATS) is standard of care for small lung resections at many centres. Computed tomography (CT)-guided insertion of microcoils can aid surgeons in performing VATS resections for non-palpable lung nodules deep to the lung surface. METHODS: Retrospective analysis of CT-guided microcoil insertions prior to VATS lung resection at a single institution from October 2008 to January 2014. RESULTS: A total of 63 patients were included (37% male, mean age 61.6±11.4 years). Forty-two patients (67%) had a history of smoking, with 10 current smokers. Sixty one (97%) patients underwent wedge resection and 3 (5%) patients had segmentectomy. Three (5%) patients required intra-operative staple line re-resection for positive or close margins. Eleven (17%) patients had a completion lobectomy, 5 of which were during the same anaesthetic. The average time between the CT-guided insertion and start of operation was 136.6±89.0 min, and average operative time was 84.0±53.3 min. The intra-operative complication rate was 5% (n=3), including 1 episode of hemoptysis, and 2 conversions to thoracotomy. The post-operative complication rate was 8% (5 patients), and included 2 air leaks, 1 hemothorax (drop in hemoglobin), 1 post chest tube removal pneumothorax, and one venous infarction of the lingula after lingula-sparing lobectomy requiring completion lobectomy. . Average post-operative length of stay was 2.2 days. A diagnosis was made for all patients. CONCLUSIONS: CT-guided microcoil insertion followed by VATS lobectomy is safe, with short operative times, short length of stay and 100% diagnosis of small pulmonary nodules. This technique will become more important in the future with increasing numbers of small nodules detected on CT as part of lung cancer screening programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".