An application of COX model for evaluation of the prognostic factors in patients with breast cancer
Bibliographic record
Abstract
Objective:To explore survival status of patients with breast cancer for 5 years after operation and assess the prognostic factors.After eliminating confouding factors.Methods: All data were collected from the hospital records of 1 002 breast cancer patients who were initially treated at the First Teaching Hospital of Norman Bethune University of Medical Sciences (FTH, Changchun China, 116 cases) and the Saint Sacrement Hospital (SSH, Quebec City Canada, 886 cases), respectively, by use of Historical cohort survey, and adjusted hazard ratios (AHR) with 95% confidence intervals were provided by use of Cox model to eliminate the confounders and estimate the changes in hazard ratio across levels of each variable (factor) , so as to assess the prognostic effects of variables across levels. Results:After adjustment, the mortality of patients treated in FTH was very similar to that in HSS (AHR=0.9, P =0.502 4); Age at operation was not significantly related to the mortality of patients after surgery(AHR=0.8, P =0.209 7 ); Patients with larger tumor size(2.0 cm) have about two times of mortality compaing to patients with smaller tumor size (≤2.0 cm) (AHR=2.2, P =0.000 1); and the mortality among women with lymph node involvement was (three times) higher than those without lymph node involvement (AHR=3.0 , P =0.000 1).Conclusion:Tumor size and number of lymph nodes involvement are very important for patient′s prognosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".