Serum LAMC2 enhances the prognostic value of a multi-parametric panel in non-small 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: Non-small cell lung cancer (NSCLC) lacks reliable serological biomarkers for predicting patients' survival and response to treatment. The present study examined the capability of serum LAMC2 and four known tumour markers for disease prognosis and patients' risk stratification. METHODS: LAMC2, CA 125, CEA, CYFRA 21-1 and SCC levels were retrospectively measured in sera obtained from 127 patients diagnosed with NSCLC by commercial immunoassays. Prognostic performance of the markers was compared with established clinical parameters and multivariate models were constructed to assess the prognostic complementarity of variables. RESULTS: LAMC2 showed significant prognostic ability for overall survival (hazards ratio: 1.607, 95% confidence interval: 1.268-2.037, P<0.0001) in the full cohort. LAMC2 and CYFRA 21-1 combination enhanced prognostic models based on common clinical parameters (c-index: 0.81 vs 0.72, P=0.00018), further enabling stratification of patients into clear risk groups. A bootstrap-based cross-validation analysis was supportive of our findings. Combination of LAMC2 and CA 125 showed similar performance. CONCLUSIONS: Our preliminary study proposes LAMC2 as a novel NSCLC prognostic factor. LAMC2 combined with CA 125 and CYFRA 21-1 could aid in clinical prediction of NSCLC patients' overall survival and inform clinical practice. Larger studies are necessary to unravel LAMC2's full potential as a new NSCLC biomarker.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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