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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".