Generalized longitudinal data analysis, with application to evaluating hospital utilization based on administrative database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many practical situations where subjects can experience recurrence of an event, the event has non-negligible duration, and both the rate of the event occurrences and the accumulative event duration are of particular interest. Well-developed methods for recurrent events analysis do not take into account the event duration, which could lead to undesirable inferences in the situations. Motivated partly by the research project with BC Cancer Agency to evaluate the hospital utilization of young cancer survivors, we develop a method to analyze recurrent event data with adjustment for event duration. Our methodology can be viewed as an extension of the well-established approaches for recurrent events. We also propose an approach to fitting semiparametric models for a general response process, which includes counting process as a special case. Data from the cancer project are used throughout the thesis to illustrate our formulation and approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it