Analysis of Mortality in a Small Sample of Older Adults
Bibliographic record
Abstract
This paper presents an analysis of mortality in a sample of 441 adults aged 55 to 85 at baseline, who were observed for eight years. Subjects living in London, Ontario, Canada, were recruited in 1987 using an approach designed to include at least 35 individuals in each of the six quinquennial age groups from 55 to 85, with roughly equal numbers of males and females. Considerable information was collected on each subject. Demographic and lifestyle data were obtained using a questionnaire, and a variety of physical measurements were taken in a laboratory. We use the well-known proportional hazards model to assess the impact of these variables on mortality and to quantify mortality rates as a function of age and sex. The variability of the latter results is examined using simulation. Since the sample size is very small by actuarial standards, we are careful about the quantitative conclusions that are made. 1 1
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".