Learning curve for completely thoracoscopic anatomic sublobar resection
Bibliographic record
Abstract
BACKGROUND: Minimally invasive anatomic sublobar resection is increasingly being considered as an alternative to lobectomy in selected cases. However, this remains a technically challenging procedure and only 5 studies evaluating learning curves have been published to date. The aim of this study was to evaluate a single surgeon's learning curve for completely thoracoscopic anatomic sublobar resection. METHODS: A retrospective review was conducted of all thoracoscopic anatomic sublobar resections by one surgeon proficient in VATS lobectomy between January 2015 and January 2020. The primary outcome was operative time. Secondary outcomes were perioperative complications, duration of chest tube drainage and length of stay. RESULTS: There were 67 thoracoscopic anatomic sublobar resections performed in 66 patients. A Time-series plot and Cumulative Sum analysis of operative times showed a drop off after case 32, suggesting achievement of competency. After case 32, mean operative times were decreased (128.59±32.42 min. vs. 153.63±40.16 min, P=0.013) and there was a trend toward decreased blood loss (124.26±76.0 vs. 175.0±141.99 mL, P=0.073). A percentage 13.6% of patients had postoperative complications other than air leak and 88,9% of these were Clavien-Dindo class 1-2; postoperative complications were evenly distributed before and after case 32. Cumlulative Sum curves for the duration of chest tube drainage and length of stay did not show any significant change during the study period. CONCLUSIONS: This study suggests that for a surgeon proficient in VATS lobectomy, competency in completely thoracoscopic anatomic sublobar resection can be achieved after 32 cases and can be accomplished in a way that does not compromise perioperative outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".