Error in Discussion Section
Bibliographic record
Abstract
cognitive challenge has different dual-task costs across the cognitive spectrum from cognitively normal to mild AD. 6 Importantly, very demanding cognitive challenges, such as serial subtraction by sevens, can generate a paradoxical response among cognitively impaired subgroups.We have observed that some participants with advanced MCI find the task too challenging, stop doing the cognitive task, and walk faster to quickly complete the test.5 Thus, demanding cognitive challenges can be better suited for cognitively healthy individuals.Prospective studies should explore further this question.We agree that enhancing the value of DTG as a cognitive biomarker may include using quantitative gait parameters, such as gait variability.We performed these analyses in our study and found that higher gait variability under DTG, measured as the coefficient of variation (mean/SD) of stride time, increased the risk of incident dementia, supporting the notion that stride time variability is a sensitive measure of brain gait control.5 However, the hazard ratios were not larger than dual-task cost in gait velocity, and the different cognitive tasks yielded different results.Gait variability, as a sensitive marker of motor control, might be better suited for populations with less impairment because it has been shown to predict outcomes such as falling in older adults with normal gait velocity and no history of falling.7 Deciphering these 3 knowledge gaps will increase the generalizability of DTG testing beyond populations with MCI and confirm the specific value of additional quantitative gait parameters for dementia prediction.In the meantime, the DTG test can be used as a motor biomarker to stratify older adults with MCI and detect those who may benefit in neuroprotective clinical trials that aim to delay the clinical onset of dementia.
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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 | 0.007 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.492 | 0.333 |
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".