Statistical approach to mediastinal staging in NSCLC with M.E.S.S.i.a. software
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Bibliographic record
Abstract
The exclusion of pathological involvement of mediastinal lymph nodes in patients affected by NSCLC plays a central role in assessing their prognosis and operability. Ceron et al. developed a software - called M.E.S.S.i.a (Mediastinal Evaluation with Statistical Support; instan approach) - that allows the calculation of the residual probability of lymph node involvement after a certain number of tests has been done, by integrating every test result with the pre-test prevalence. M.E.S.S.i.a. bridges a gap of current American College of Chest Physicians (ACCP) guidelines, providing probability values of mediastinal metastasis for a correct clinical decision. We conducted a preliminary retrospective study in a series of 108 patients affected by non small cell lung cancer (NSCLC). Pathological staging was compared to the probability of nodal involvement calculated by M.E.S.S.i.a. software. Forty-two out of 108 subjects (39%) had a calculated post-test probability <8%; none of these had proven N2/N3 metastasis at surgical staging (negative predictive value, NPV: 100%). In 12/41 cases M.E.S.S.i.a. was able to avoid invasive procedures. The remaining 66 (61%) patients did not reach the surgical threshold; among these, 11 displayed N2 positivity at pathological staging. Receiving operator curve (ROC) analysis produced an area under curve (AUC) value of 0.773 (p<0.001). These preliminary data show high accuracy of M.E.S.S.i.a. software in excluding N2/N3 lymph node involvement in NSCLC. We have therefore promoted a prospective multicenter study in order to to get a validation of the calculator at different levels of probability of lymph node involvement. The recruitable subjects are potentially operable NSCLC patients; the gold standard for detection of mediastinal disease is the surgical lymph node dissection.
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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