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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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 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".