A method of assessing reasons for conversion during video-assisted thoracoscopic lobectomy
Bibliographic record
Abstract
Conversion rates during video-assisted thoracoscopic lobectomy are reported, but no previous publications have classified the cause of conversion. The aim of the study was to develop a quality assessment tool [vascular, anatomy, lymph node, technical (VALT) 'Open'] to evaluate reasons and nature of conversion during the development of a video-assisted thoracoscopic lobectomy program. Between 2006 and 2008, 237 patients with a median age of 65 years underwent video-assisted thoracoscopic lobectomy primarily for lung. The number of video-assisted thoracoscopic lobectomy cases over open cases has increased over the period. Conversion rate has dropped from 15% (2006) to 11% (2008). A total of 32 cases required conversion. The VALT 'Open' classification for reason to convert and nature of conversion was used. The average length of stay was shorter for non-converted cases. No uncontrolled conversions where the patient was unstable were required, and in the 14 cases converted following some difficulty, such as pulmonary artery injury. A pattern to the learning curve became predictable. The quality assessment tool used (VALT 'Open') will allow cause of conversion and nature of conversion to be tracked and audited during the development of a video-assisted thoracoscopic surgery lobectomy program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".