Competing risk events determining probability of cause-specific failure in nasopharyngeal carcinoma
Bibliographic record
Abstract
10082 Background: The commonly employed Kaplan-Meier (KM) method is based on the assumption that different failure types (local-regional, distant, etc.) are independent. In reality, these failures occur at different stages in disease progression and are strongly correlated with each other. The assumption of independence of different failure types may violate certain assumptions in the modeling, and hence may affect the clinical interpretation and treatment selection. A better approach to estimate cause-specific failure probability is to calculate cumulative incidence rates by taking into account other events within a competing risk framework, in which the dependency of different failures are considered. Methods: The data was based on a large retrospective cohort study conducted at the Prince of Wales Hospital in Hong Kong, China, in 1996–97. 945 patients with nasopharyngeal carcinoma (NPC) had been treated with a standard protocol and been followed up regularly with a median follow-up period of 69 months (1–122 months). We calculated the cumulative incidence rates of local-regional failure and distant metastasis, and compared the result against KM method. In competing risk analysis, local regional failure, distant metastases and death were considered as competing events during the remaining lifetime of NPC patients from first presentation. Results: The probability of local-regional failure and distant metastasis was higher by KM method than by competing risk method. The result indicated that KM analysis overestimated event rate and the difference became larger in a longer follow-up period, when more competing events occurred. Conclusion: Kaplan-Meier analysis overestimates the probability of cause-specific failure. Competing risk analysis provides us a more accurate method in the determination of the pattern of failure. It provides better evidence to clinicians to enable them to predict the prognosis and select proper therapy. [Table: see text] No significant financial relationships to disclose.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".