Imputation of Missing Dates of Death or Institutionalization for Time-to-Event Analyses in the Canadian Study of Health and Aging
Bibliographic record
Abstract
Data from the Canadian Study of Health and Aging (CSHA) allow investigators to study patterns and predictors of mortality and institutional placement in a well characterized, population-based cohort of elderly Canadians. However, it is impossible to study the timing of these events if the date of occurrence is missing. This technical article describes a procedure for imputing missing dates of death or institutionalization. The first step consists in identifying and correcting dates that are inconsistent with other available dates on which we know the event has or has not occurred. A missing date for an event is then replaced by the middle of a range of plausible dates for its occurrence. This constitutes a valuable addition to the CSHA data since it precludes the loss of information that results from discarding subjects with missing occurrence dates in time-to-event analyses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".