EXPLORING CIND STABILITY ACROSS TIME: INSIGHTS FOR IMPROVING COGNITIVE IMPAIRMENT CLASSIFICATIONS
Bibliographic record
Abstract
Abstract Objective Cognitive impairment no dementia (CIND) classifications are susceptible to false positives and may not be indicative of progressive cognitive decline or dementia risk. The current project sought to investigate the dynamic nature of CIND across six years and to elucidate whether baseline impairment severity (single vs. multidomain impairment) predicts CIND stability patterns such as reversion and fluctuation between cognitive statuses. Method: Utilizing data from Project MIND, participants (N=259; 65-90 years) were classified as either Normal Cognition (NC) or CIND across five annual assessments using a battery of five cognitive tasks. Participants were classified as either Stable NC, Stable CIND, Progressors (NC—CIND), Reverters (CIND—NC) or Fluctuaters (CIND—NC—CIND) according to their across-time stability patterns. Participants initially classified as CIND were further subdivided based on impairment severity: single-task (CIND-S) or multiple-task (CIND-M) impairment. Multinomial logistic regression was utilized to explore whether baseline CIND severity differentiated Reverters and Fluctuaters from those who remained Stable CIND. Results Most individuals were unstable in their CIND status for several years following baseline assessment. Compared to those classified as Stable CIND, Fluctuaters (-1.52, p=.01) and Reverters (-1.68, p=.01) were more likely to be classified as CIND-S (72% and 74%, respectively). Conclusions: Reverting back to NC or fluctuating between cognitive statuses is partly a function of instability in CIND-S classifications. Moving forward, CIND classifications could be improved through careful consideration of CIND severity at initial assessment. In particular, those classified as CIND-M may represent an at-risk group most likely to exhibit progressive cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".