Bibliographic record
Abstract
We would like to thank Dr Baisi et al. [1] for their insightful comments on our paper, specifically regarding the discrepancy in cancer-specific survival between matched patients who underwent lung cancer resection via VATS lobectomy vs open lobectomy [2]. Although no statistical difference could be detected in cancer-specific survival, the survival curves seem to show a slight divergence in favour of open lobectomy. This apparent divergence can be explained by two potential theories. The first is that it is due to chance. One will always wonder whether a significant difference may have been detected if a larger population sample was available, and whether a study with enough power can be actually achieved. The second theory is that better cancer-specific survival can be attributed to better lymph node dissection in the open group. Although we did not find a difference in the number of lymph nodes harvested between the two groups, larger database studies have clearly demonstrated that the rates of N1 lymph node harvest in VATS lobectomy is inferior to open lobectomy [3]. Speculation remains around whether better lymph node harvesting translates into better survival. We agree with Dr Baisi et al. [1] in restricting our conclusions to early-stage lung cancer. Although other groups have demonstrated the feasibility of VATS resection for large or locally advanced tumours [4], our data include only Stage I and II patients, with a predominance of Stage I disease. We are grateful to Dr Baisi et al. [1] for their kind and insightful comments, and we thank them for taking the time to remark on our work.
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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 | 0.009 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.046 | 0.056 |
| Insufficient payload (model declined to judge) | 0.006 | 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".