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Record W2127381409 · doi:10.1093/ejcts/ezu450

Re: The burden of death following discharge after lobectomy

2014· letter· en· W2127381409 on OpenAlexaboutno aff
Pierre‐Emmanuel Falcoz

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2014
Typeletter
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.030
GPT teacher head0.290
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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