Cox Proportional Hazard Regression Interaction Model and Its Application to Determine The Risk of Death in Breast Cancer Patients after Chemotherapy
Bibliographic record
Abstract
Introduction: This research is based on medical record data of breast cancer patients who seek treatment at the Central General Hospital, dr. Sardjito Yogyakarta, from 2018-2021 has as many as 105 patients. Several risk factors for cancer include demographic factors, clinical factors, tumor factors, and therapy. These factors lead to different psychological states of patients, resulting in the rate of recovery and death of patients. Objective: To determine the risk of death in breast cancer patients after chemotherapy. Methods: The method used in this study is Cox Proportional Hazard survival analysis with an interaction model. The variables studied were age, marital status, profession, insurance, BMI, comorbidities, duration of chemotherapy, chemotherapy agent, chemotherapy type, and tumor size. Results: The analysis results using SPSS software obtained the best hazard and survival model with four significant variables, namely the duration of chemotherapy, chemotherapy agents, chemotherapy types, and the interaction between BMI and chemotherapy types. Conclusions: The most significant risk factor for death was palliative chemotherapy type with HR 27.195 and 3-5 chemotherapy agents with HR 4.997. Meanwhile, the long duration of chemotherapy and the interaction between lean BMI and palliative chemotherapy reduced the risk of death by HR 0.967 and 0.128, respectively.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".