The burden of death following discharge after lobectomy
Bibliographic record
Abstract
OBJECTIVES: Pulmonary lobectomy is the most commonly performed surgery for lung cancer and remains the gold standard operative treatment. The reported surgical mortality from this procedure rarely differentiates between in-hospital mortality (IHM) and early post-discharge mortality (PDM). We aimed to examine the IHM and 90-day PDM over time and identify outcome predictors including patient characteristics, comorbidity and system-level factors. METHODS: Data for patients who underwent lobectomy from 2005 to 2011 were acquired from a linked Ontario population-based database. Exclusions included patients undergoing sleeve lobectomy, resections for synchronous lesions, previous lung malignancy and extended length of stay (LOS) over 30 days. We reported proportional mortality and cumulative survival attributable to IHM and PDM with confidence intervals. Multivariate logistic and Cox regression analyses were performed to examine the role of variables associated with IHM and 90-day PDM. RESULTS: For 5389 patients who underwent lobectomy for non-small-cell lung cancer, the median LOS was 6 (1-30) days. IHM (n = 73) was 1.4% (1.1-1.6%) and PDM (n = 101) was an additional 1.9% (1.6-2.3%) within 90 days post-lobectomy discharge. Logistic regression suggested that age [odds ratio (OR): 1.5 (1.3-1.8)], myocardial infarction [OR: 3.6 (1.8-7.0)], congestive heart failure [OR: 5.8 (2.4-13.8)], chronic obstructive pulmonary disease [OR: 1.9 (1.1-3.2)], preoperative positron emission tomography [OR: 2.7 (1.1-7.0)], peptic ulcer disease [OR: 22.1 (4.1-117.4)], hemiplegia [OR: 15.8 (1.8-141.1)], other primary cancer [OR: 0.5 (0.3-0.8)] and year of surgery [OR: 1.0 (0.8-1.0)] were potential predictors of IHM. Length of hospital stay [hazard ratio (HR): 1.1 (1.0-1.1)], male gender [HR: 1.5 (1.0-2.3)], age [HR: 1.1 (1.0-1.3)] and metastatic cancer [HR: 2.6 (1.7-4.0)] were potential predictors of PDM. CONCLUSIONS: PDM represents a substantive, under-reported burden of mortality due to lobectomy. More than half of post-lobectomy mortality occurs post-discharge and the annual rate remained unchanged, while IHM decreased with time, suggesting that the improvement seen in mortality might be exclusive to the smaller IHM. Patient factors play a significant role in both IHM and PDM. We emphasize that this identifies the importance of appropriate patient selection, further investigation of risk factors and particular attention to these risk factors during regular follow-up visits to improve PDM in this high-risk patient population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".