Joint Occurrences of Competing Risks and Multivariate Longitudinal Data: A Prediction Investigation for the HIV. long Data
Bibliographic record
Abstract
In this article, some prediction strategies are introduced for event times, where multivariate data with competing or semi-competing risks are simultaneously collected. Without loss of generality, the proposed methods can be used to analyze multivariate longitudinal data with competing or semi competing risks, often encountered in social sciences and sports activities. Regarding the situations mentioned earlier, we can provide the prediction values of: I. Time of occurrences of any cause for specific individuals II. Time of subsequent events for some cause in other individuals III. The covariate values on predicted time of I and II. Accordingly, doctor assistants or nurses can schedule good visiting times based on I and II. Item III can provide the missing values of all covariates that are utilized for better modeling. The corresponding statistical background is extensively discussed. Finally, an actual data set has been analyzed, the prediction values are provided, and their performances are assessed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".