Initial presentation of lung cancer in the emergency department: a descriptive analysis
Bibliographic record
Abstract
Background: Guidelines aimed at improving care for lung cancer, the leading cause of cancer-related death in Canada and worldwide, require accurate knowledge of the diagnostic setting or pathway. We sought to determine how often lung cancer is initially diagnosed through the emergency department. Methods: We performed a descriptive study that included all cases of primary lung cancer diagnosed in residents of Nova Scotia in 2014. Cancer registry data included diagnostic data and date of death to Aug. 31, 2016. We reviewed linked hospital records, including laboratory and imaging results, to identify the first positive diagnostic study and the route of presentation (emergency department v. other). We evaluated time from diagnosis to death as a function of presentation route using Kaplan–Meier curves and Cox regression (hazard rate ratios [HRRs]). Results: Sufficient data were available for 946 of 951 cases identified, of which 336 (35.5%) were diagnosed through the emergency department. Cases diagnosed via the emergency department were more likely to be at an advanced stage (stage IV, 59.5% v. 43.4%), with patients experiencing shorter survival (1-yr survival, 28.4% v. 49.5%), including stage-specific survival. Mortality for cases diagnosed in the emergency department was 54% higher than for the non–emergency department group after adjusting for age and stage (HRR 1.54, 95% confidence interval 1.32–1.81). Few patients (7.1%, n = 24) who presented to the emergency department reported having no family physician. Interpretation: The emergency department is a common route of presentation for lung cancer and is associated with advanced stage at diagnosis and reduced survival time. Strategies are needed to encourage pre-emergent diagnosis and to ensure that emergency providers are supported in the initial care of patients with lung cancer.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".