Predicting Overall Survival in Patients with Multiple Primary Lung Cancer: Nomogram Development and Validation Study (Preprint)
Bibliographic record
Abstract
Background: With the rapid development of medical technology and the emphasis on early lung cancer screening, the detection rate of multiple primary lung cancer (MPLC) has increased in recent years. However, the prognostic determinants and clinical characteristics of patients with MPLC remain poorly characterized. Objective: This study aimed to develop and validate a nomogram for predicting overall survival (OS) in patients with MPLC using data from the Surveillance, Epidemiology, and End Results database. Methods: This study was reported in accordance with the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines. A cohort of 4177 patients with MPLC (2007-2015) was obtained from the Surveillance, Epidemiology, and End Results database. The patients were randomly divided into training (n=2923) and validation (n=1254) cohorts at a 7:3 ratio. Backward stepwise Cox regression identified 11 independent risk factors, which were integrated into a nomogram predicting 3-, 5-, and 8-year OS rates. Results: The nomogram demonstrated superior discriminative ability compared to the American Joint Committee on Cancer staging system, with higher area under the receiver operating characteristic curve values for 3-, 5-, and 8-year OS predictions in both cohorts (training cohort: 0.743, 0.751, and 0.759, respectively; validation cohort: 0.737, 0.734, and 0.695, respectively). Calibration curves and decision curve analysis confirmed its clinical utility. Conclusions: This study establishes a validated nomogram incorporating clinical and socioeconomic variables to optimize prognostic assessment and personalized treatment planning for patients with MPLC.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".