A prognostic model for advanced stage nonsmall cell lung cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A pooled analysis was performed to examine the impact of pretreatment factors on overall survival (OS) and time to progression (TTP) in patients with advanced-stage nonsmall cell lung cancer (NSCLC) and to construct a prediction equation for OS using pretreatment factors. METHODS: A pooled data set of 1053 patients from 9 North Central Cancer Treatment Group trials was used. Age, gender, Eastern Cooperative Oncology Group performance status (PS), tumor stage (Stage IIIB vs. Stage IV), body mass index (BMI), creatinine level, hemoglobin (Hgb) level, white blood cell (WBC) count, and platelet count were evaluated for their prognostic significance in both univariate and multivariate analyses by using a Cox proportional-hazards model. RESULTS: Patients who had high WBC counts, low Hgb levels, PS >0, BMI < 18.5 kg/m2, and TNM Stage IV disease had significantly worse TTP and OS. Patients who had Stage IV disease with a high WBC count had a particularly poor prognosis. An equation to predict the OS of patients with Stage IV NSCLC based on pretreatment PS, BMI, Hgb level, and WBC count was constructed. CONCLUSIONS: In addition to the widely accepted prognostic factors of PS, BMI, and disease stage, both of the readily available laboratory parameters of Hgb level and WBC count were found to be significant prognostic factors for OS and TTP in patients with advanced-stage NSCLC. The authors' prediction equation can be used to evaluate the benefit of a treatment in Phase II trials by comparing the observed survival of a cohort with its expected survival by using the patients' own prognostic factors in place of comparisons with historic data that may have substantially different baseline patient characteristics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it