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Record W2045760620 · doi:10.1093/ejcts/ezu427

The burden of death following discharge after lobectomy

2014· article· en· W2045760620 on OpenAlexafffundabout
Laura Schneider, Forough Farrokhyar, Colin Schieman, Waël C. Hanna, Yaron Shargall, Christian Finley

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersEuropean Society for Medical OncologyGovernment of OntarioInstitute for Clinical Evaluative Sciences
KeywordsMedicineLung cancerMalignancyOdds ratioInternal medicineMyocardial infarctionLogistic regressionComorbidityConfidence intervalPneumonectomySurgeryPopulation

Abstract

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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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.282
Teacher spread0.264 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2014
Admission routes3
Has abstractyes

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