Bibliographic record
Abstract
The current issue of the European Journal of Cardio-Thoracic Surgery publishes the remarkable work of Schneider et al. [1], which contributes to a deeper understanding of actual postoperative mortality—presently differentiated as in-hospital mortality (IHM) and 90-day post-discharge mortality (PDM)—in the framework of pulmonary lobectomy for lung cancer. For this purpose, data from patients who underwent lobectomy over a 7-year period (2005–11) were queried from an Ontario population-based database. Of 5389 patients who underwent lobectomy for non-small-cell lung carcinoma (NSCLC), the median length of stay was 6 (1–30) days. IHM (n = 73) was 1.4% (1.1–1.6%) whereas PDM (n = 101) was an additional 1.9% (1.6–2.3%) within 90-day post-lobectomy discharge. When searching for potential predictors of mortality, the following eight variables were found to be significant predictors of IHM: age [odds ratio (OR) = 1.5], myocardial infarction (OR = 3.6), congestive heart failure (OR = 5.8), chronic obstructive pulmonary disease (OR = 1.9), preoperative positive emission tomography (OR = 2.7), peptic ulcer disease (OR = 22.1), hemiplegia (OR = 15.8) and other primary cancer (OR = 0.5). In addition, logistic regression showed that length of hospital stay [hazard ration (HR) = 1.1], male gender (HR = 1.5), age (HR = 1.1) and metastatic cancer were potential predictors of 90-day PDM. The authors concluded that PDM represents a substantive under-reported burden of mortality due to lobectomy. Patient factors play a significant role in both IHM and PDM. Finally, they emphasize that 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. The current article is the ‘mirror manuscript’ of another work published by the same team from the same Ontario database, on pneumonectomy [2]. In addition, numerous studies dealing with PDM after resection for lung cancer have been recently published [3–5], showing that this issue is gaining notoriety and concern within the surgical field. Schneider et al. [1] nicely demonstrated that patients undergoing lobectomy for NSCLC experience a greater risk of death after discharge from hospital compared with when they are admitted as in-patients. This raises legitimate questions as to the actual delivery of quality care and firstly, whether anything might have been done differently at the time of the discharge evaluation or immediately after discharge to prevent these deaths. As pointed out by McMillan et al. [3], a closer monitoring after discharge with earlier and repeated follow-up visits, better coordination with primary physicians and regular home visits by care providers in these high-risk patients could lead to earlier detection of problems and improvement outcomes, but there is no evidence yet to support this strategy in the literature. However, according to this stimulating article's results, we implicitly understand that a deep exploration of the post-discharge timeframe would need to be urgently undertaken to try to elucidate reasons for this elevated mortality. This might be the clue not only to more precisely assess already known predictive factors of mortality on which prevention and/or potential intervention can be done (targeted arrhythmia management, venous thrombo-embolism prophylaxis, enhance care of elderly population, for example), but also, going one step further, to shed light on a probable insufficiency of the healthcare system and try to reverse this weakness in a well-structured postoperative quality improvement care programme. For this purpose, the recommendation that national or international registries—such as the Society of Thoracic Surgeons or European Society of Thoracic Surgeons databases—consider the inclusion of 90-day mortality in their data collection to be of major importance. Indeed, it may well contribute in the near future to a powerful analysis of this critical period, by providing a more reliable and precise estimate of death rates and their potential predictors. In the meantime, Schneider et al. are to be congratulated on their investigations in this area. From the standpoint of medical care, their results will certainly prove to be most beneficial to the thoracic surgery community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".