Survey of Patients with Cervical Cancer in Hospital UniversitiSains Malaysia: Survival Data Analysis with Time-Dependent Covariate.
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the third most common cancer among women in Malaysia. The objective of this study was to estimate the effect of explanatory variables on survival time of cervical cancer patients receiving treatment at a hospital in Malaysia. METHODS: In this retrospective record review study, cervical cancer data obtained from Hospital UniversitiSains Malaysia (HUSM) was analysed. The data comprises of 120 patients who had been diagnosed as cervical cancer between 1(st) July 1995 and 30(th) June 2007, and obtained treatment from the hospital. The outcome variable was survival time (in months) from cervical cancer diagnosis to death. A stratified Weibull model was applied to study the effect of explanatory variable on survival time when there was time-dependent covariate in the model. RESULTS: Stage of disease and metastases were important prognostic variables. However, metastasis had been stratified because this variable did not satisfy the proportional hazard assumption. In without metastasis stratum, patients who were diagnosed at stage III & IV are at 2.30 times the risk of death as those in stage I & II. Meanwhile, in with metastasis stratum, patients in stage III & IV group had 3.53 times the hazard faced by patients in stage I & II. CONCLUSION: The prognosis of cervical cancer patients was dependent upon the stage at diagnosis, after the stratification of the metastasis variable. A poorer prognosis on survival was observed for patients in stage III & IV than those in stage I & II.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".