How Much Time Can be Saved in Cognitive Decline? the Internet‐based Conversational Engagement Clinical Trial (I‐CONECT)
Bibliographic record
Abstract
Abstract Background Clinical trials should strive to yield results that are clinically meaningful rather than solely relying on statistical significance. However, the determination of clinical meaningfulness of dementia clinical trials lacks standardization and varies based on the trial’s nature. To tackle this issue, a proposed approach involves assessing the time saved before reaching a specific threshold in cognitive status. In this study, we investigated the time saved in cognitive decline among the top responders based on the individual‐level treatment responses (ITR) analysis, using data from the Internet‐based Conversational Engagement Clinical Trial (I‐CONECT; NCT02871921). Method I‐CONECT is a randomized controlled trial to examine the effects of conversational interactions on cognition among socially isolated participants aged ≥ 75 years old. The experiment group engaged in video chats with study staff 4 times/week for 6 months; the control groups received weekly check‐in phone calls. We focused on cognitive outcomes that exhibited significant treatment effects at 6‐month follow‐up: the Montreal Cognitive Assessment (MoCA) for global cognition and Category Fluency Animals (CFA) for semantic fluency. To assess ITR, we employed 300 iterations of 3‐fold cross‐validated random forest models. We estimated treatment heterogeneity by conducting permutation tests on the area between curves (ABC) statistics derived from the ITR scores. We estimated time saved in cognitive decline as the difference in the number of months required for the top responders (top 25%; 33%) and the remaining participants to reach the same cognitive level at 6‐months follow‐up. Result ABC statistics showed substantial heterogeneity in treatment response with MoCA but modest heterogeneity in treatment response with CFA. For global cognition, assuming a treatment effect size between 30‐50%, the top 25% and 33% of responders exhibited potential cognitive delays ranging from 4.1 to 10.8 months and 5.9 to 13.9 months, respectively (Figure 1). For semantic fluency, large effect sizes (70‐100%) are required to show potential cognitive delays ranging from 0.5 to 6.7 months. Conclusion Individual differences in the time saved for cognitive decline through the ITR analysis are meaningful outcomes in clinical trials. Future trial outcomes should consider both quantity and quality concepts such as quality‐adjusted time saved.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".