Multistate models for comparing trends in hospitalizations among young adult survivors of colorectal cancer and matched controls
Bibliographic record
Abstract
BACKGROUND: Over the past years, the incidence of colorectal cancer has been increasing among young adults. A large percentage of these patients live at least 5 years after diagnosis, but it is unknown whether their rate of hospitalizations after this 5-year mark is comparable to the general population. METHODS: This is a population-based cohort consisting of 917 young adult survivors diagnosed with colorectal cancer in Ontario from 1992-1999 and 4585 matched cancer-free controls. A multistate model is presented to reflect and compare trends in the hospitalization process among survivors and their matched controls. RESULTS: Analyses under a multistate model indicate that the risk of a subsequent hospital admission increases as the number of prior hospitalizations increases. Among patients who are yet to experience a hospitalization, the rate of admission is 3.47 times higher for YAS than controls (95% CI (2.79, 4.31)). However, among patients that have experienced one and two hospitalizations, the relative rate of a subsequent admission decreases to 3.03 (95% CI (2.01, 4.56)) and 1.90 (95% CI (1.19, 3.03)), respectively. CONCLUSIONS: Young adult survivors of colorectal cancer have an increased risk of experiencing hospitalizations compared to cancer-free controls. However this relative risk decreases as the number of prior hospitalizations increases. The multistate approach is able to use information on the timing of hospitalizations and answer questions that standard Poisson and Negative Binomial models are unable to address.
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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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".