THE PREVALENCE OF NEUROLOGICAL SYMPTOMS AMONG CHINESE OLDER ADULTS IN THE GREATER CHICAGO AREA
Bibliographic record
Abstract
applicable. The Prentice-Gloeckler (PG) model, which adapts the Cox-PH model to grouped survival data, is more appropriate, but has fewer resources. It is important to compare the PG and Cox-PH models because if they yield very similar results, we may use the Cox-PH model as a substitute for the PG model. In particular, we can utilize the many prediction modeling approaches developed for Cox-PH models. Methods: Through simulation, we compared the PG and Cox-PH models, when the data are grouped, in terms of bias and standard error of the regression coefficient estimators. We also compared the models when predicting time-to-frailty using data from the Cardiovascular Health Study (N=5888). Results: The regression coefficients were nearly identical for Cox-PH and PG models. The bias was negligibly small and essentially the same. Standard errors were also very similar between the models. For example, in a representative simulation study, bias was only 0.03 lower and standard error only 0.01 higher for the PG model. Conclusions: Age-related phenotypes are generally observed at discrete time intervals. Our results show that the PG and Cox-PH models provide similar results when applied to such data. Therefore, aging researchers can use the Cox-PH model and take advantage of the vast modeling resources available to it.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".