Challenges and Strategies in the Analysis of Multiple Events in Oncology
Bibliographic record
Abstract
Patients with bone metastases are at risk for a variety of skeletal complications ( 1 ), each of which can lead to a significant reduction in patient quality of life and considerable expense to the health care system. Skeletal complications are multifactorial in nature and typically include vertebral fractures, nonvertebral fractures, spinal cord compression, episodes of bone pain requiring radiation therapy, and surgery for the prevention or treatment of fractures. Each of these complications can occur repeatedly over time. The mechanisms causing skeletal complications are complex biological processes. The objective of this chapter is to discuss some statistical concepts for the analysis of the clinical complications resulting from metastases to bone and some basic methods of analysis. Concepts to be discussed include the use of composite end points, the analysis of recurrent clinical events, heterogeneity in the clinical course of bone complications, and the need to address the link between the propensity for bone complications and survival time.
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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 | 0.104 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".