Risk stratification of lung cancer patients at initial presentation: A retrospective cohort study.
Bibliographic record
Abstract
e20062 Background: Early detection and treatment of non-small cell lung cancer (NSCLC) has been shown to improve survival. Current screening guidelines focus on at-risk populations, overlooking a significant proportion of patients (pts) who will develop NSCLC. There is a need for further risk stratification in this group. Methods: A retrospective cohort analysis was conducted on pts referred to the BC Cancer Agency – Vancouver Centre for NSCLC. Records were reviewed for the date of first abnormal imaging and 6 clinical factors (CF) noted by the referring clinician at initial presentation. CFs were: ECOG PS > 2, new-onset dyspnea > MRC 3, chest pain, hemoptysis, weight loss > 10% and systemic symptoms (seizure, bone pain, or paraneoplastic syndrome). Individuals meeting current low-dose CT screening criteria (age 55-74, 30 pack-year smoking history within the last 15 years) were also identified. Results: 435 cases were identified from Jan 1 to Dec 31, 2013; 308 had sufficient information to be included for analysis. Median age: 69; smoking history: 69%; stage: I = 5%, II = 9%, III = 26%, IV = 60%. Multivariate analysis identified 4 of 6 CF were associated with worse overall survival (OS, p < 0.05); hemoptysis and weight loss were not significant predictors and were not retained for analysis. Cases were stratified based by the number of CFs. Pts with no CF had significantly improved OS (median 30.5 mo) compared to those with 1 (12.1 mo), 2 (8.1 mo) or 3-4 (2.5 mo; p < 0.001 for all comparisons) CF. Screening criteria were met for 94 pts (31%). For the other 214 pts (69%), number of CF was 0 = 29%, 1 = 29%, 2 = 33%, 3-4 = 9%. OS was similar whether or not pts were eligible for screening. In the subset of ineligible pts, CFs retained their predictive value (p < 0.05). Conclusions: Four clinical factors predict poor outcomes in pts presenting with abnormal imaging suspicious for NSCLC. In this population, 31% of patients would have been eligible for low-dose CT screening. An additional 49% of patients with abnormal imaging had at least one CF identifiable upon initial contact with a healthcare provider. Determination of key clinical factors may assist in risk stratification of pts ineligible for screening who warrant further investigation for lung cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".