Generalizability of cognitive results from clinical trial participants to an older adult population: Addressing external validity
Bibliographic record
Abstract
Introduction: Study inclusion criteria and recruitment practices limit the generalizability of randomized-controlled trial (RCT) results. Statistical modeling could enhance generalizability of outcomes. To illustrate this, the cognition-depression relationship was assessed with and without adjustment relative to the target population of older women. Methods: Randomized participants from four RCTs and non-randomized participants from two cohorts were included in this study. Prediction models estimated probability of being randomized into trials from target populations. These probabilities were used for inverse odds weighting relative to target populations. Weighted linear regression was used to assess the depression-cognition relationship. Results: There was no depression-cognition relationship in the combined randomized sample. After applying weights relative to a representative cohort, negative relationships were observed. After applying weights relative to a non-representative cohort, bias of estimates increased. Discussion: Quantitative approaches to transportability using representative samples may explain the absence of a-priori established relationships in RCTs.
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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 | 0.653 | 0.802 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".