Sublobar resection reduces the risk of postoperative cognitive dysfunction compared with lobectomy
Bibliographic record
Abstract
OBJECTIVES: Sublobar resection, including wedge resection and segmentectomy, is non-inferior to lobectomy in early-stage non-small cell lung cancer treatment. We aimed to compare the risk of postoperative cognitive dysfunction (POCD) between sublobar resection and lobectomy. METHODS: We conducted a prospective cohort study. Patients with sublobar resection or lobectomy were divided into the sublobar group or the lobar group, respectively. Cognition was assessed before and after surgery with Montreal Cognitive Assessment and Minimum Mental State Examination tests. POCD is defined as Z score of Montreal Cognitive Assessment change ≤-1.96. Propensity score matching (PSM) was performed to make demographics well-balanced between the 2 groups. RESULTS: A total of 335 patients were enrolled. Both the postoperative 1-day POCD rate (sublobar 5.5% vs lobar 18.2%, P < 0.001) and the postoperative 1-month POCD rate (sublobar 7.9% vs lobar 21.8%, P < 0.001) were significantly lower in the sublobar group compared with lobar group, with demographics unbalanced between the 2 groups. In the 133 demographics-matched pairs obtained by PSM, both the postoperative 1-day POCD rate (sublobar 5.3% vs lobar 17.3%, P = 0.005) and the postoperative 1-month POCD rate (sublobar 8.3% vs lobar 18.8%, P = 0.018) remained significantly lower in the sublobar group than in the lobar group. The incidences of postoperative 1-day (P = 0.109) and postoperative 1-month (P = 0.026) Minimum Mental State Examination abnormity were also lower in the sublobar group than in the lobar group but only the latter was with statistical significance after PSM. CONCLUSIONS: Sublobar resection has an advantage over lobectomy in preventing POCD. Our findings might be a reference for selecting the most suitable type of resection for non-small-cell lung cancer patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".