Commentary on: “uniportal video-assisted thoracoscopic surgery: safety, efficacy and learning curve during the first 250 cases in Quebec, Canada”
Bibliographic record
Abstract
The past two decades have seen video-assisted thoracoscopic surgery (VATS) become the preferred approach for the treatment of early stage lung cancer (1,2) (NCCN, ACCP). Traditionally performed through 2–4 small incisions, thoracoscopic resection by a single 3–4 cm incision, or uniportal VATS resections, gaining traction at many centers around the globe. The adoption of anatomic resection by a uniportal thoracoscopic approach is still in a relatively early, phase with champions and critics on both teams (3,4). Proponents of uniportal VATS lobectomy advocate that this approach is associated with decreased pain, paresthesias, and morbidity, when compared to a multiportal thoracoscopic approach, resulting in expedited recovery. Opponents of the uniportal approach intimate concerns of patient safety and a steep learning curve as a result of the technical requirements of having all instrumentation share the same incision, in addition to unresolved questions of oncologic adequacy.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.035 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".