Cox regression analysis on the survival rate of breast cancer patients
Bibliographic record
Abstract
Limited studies have been conducted on the survival analysis of breast cancer patients. And no study has been investigated using cancer datasets from the UK and Canadian patients. This study aims to qualify the factors contributing to survival time for female breast cancer patients, including patients' age, tumor size, tumor stage, mutation counts, and positive lymph nodes. The hypothesis is proposed that these factors are all associated with the increasing death rate risk for breast cancer patients. The dataset comes from a study conducted on 2510 female breast cancer patients from the UK and Canada, collected by long-term clinical follow-up. The Cox model is applied to each factor to explore their relationship with the survival of patients. All the results are tested, using Schoenfeld residuals. The coefficients between the explanatory variables and survival time are 0.033863 for age, 0.064274 for lymph nodes, 0.007031 for tumor size, 0.010202 for mutation count, and 0.243451 for tumor stage. The C-index of this model is 0.65653558. Our study suggests that on the premise of having some clinical symptoms, the Cox model can be used to predict the survival time of breast cancer patients. The study has some reference value with its convenient procedure and certain accuracy. According to the outcome of Cox regression, the most pivotal explanatory variables are age, lymph nodes examined positive, tumor size, and tumor stage. As these variables increase, the expectation of the survival time of the patients will decrease.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".