M-bioscore: proposing a new statistical model for prognostic factors in metastatic breast cancer patients
Bibliographic record
Abstract
AIM: The current study aims to propose and internally validate 'M-bioscore', which is a model to help predict the outcomes of untreated metastatic breast cancer patients. METHODOLOGY: Surveillance, epidemiology and end results (SEER) database (2010-2013) was accessed. Patients were divided into two groups: a training set and a validation set. Through a Cox proportional model, multivariate analysis for potential prognostic factors was performed. M-bioscore was calculated for all patients. Survival analyses were conducted through Kaplan-Meier analysis/log-rank testing. RESULTS: A total of 6655 metastatic breast cancer patients were analyzed. In the training set, the following factors were linked to better cancer-specific survival in multivariate analysis: estrogen receptor positivity, isolated distant nodal metastases, progesterone receptor positivity, lower nuclear grade and HER2 neu positivity (p < 0.01). Cancer-specific survival was then assessed according to M-bioscore. Adjusted Cox regression cause-specific hazard (using breast cancer death as the event of interest) was evaluated in the validation cohort. Pairwise hazard ratio comparisons between different scores were significant (p < 0.05) except for the comparison between score 6 and 7. C-index for the validation cohort was 0.665 (Standard error (SE): 0.010; 95% CI: 0.646- 0.685). CONCLUSION: M-bioscore can predict the outcomes of untreated metastatic breast cancer patients. Validation of external datasets is needed.
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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 | 0.000 |
| 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 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".