Validation of a Harmonised, Three-Item Cognitive Screening Instrument for the Survey of Health, Ageing and Retirement in Europe (SHARE-Cog)
Bibliographic record
Abstract
More accurate and standardised screening and assessment instruments are needed for studies to better understand the epidemiology of mild cognitive impairment (MCI) and dementia in Europe. The Survey of Health, Ageing and Retirement in Europe (SHARE) does not have a harmonised multi-domain cognitive test available. The current study proposes and validates a new instrument, the SHARE cognitive instrument (SHARE-Cog), for this large European longitudinal cohort. Three cognitive domains/sub-tests were available across all main waves of the SHARE and incorporated into SHARE-Cog; these included 10-word registration, verbal fluency (animal naming) and 10-word recall. Subtests were weighted using regression analysis. Diagnostic accuracy was assessed from the area under the curve (AUC) of receiver operating characteristic curves. Diagnostic categories included normal cognition (NC), subjective memory complaints (SMC), MCI and dementia. A total of 20,752 participants were included from wave 8, with a mean age of 75 years; 55% were female. A 45-point SHARE-Cog was developed and validated and had excellent diagnostic accuracy for identifying dementia (AUC = 0.91); very good diagnostic accuracy for cognitive impairment (MCI + dementia), (AUC = 0.81); and good diagnostic accuracy for distinguishing MCI from dementia (AUC = 0.76) and MCI from SMC + NC (AUC = 0.77). SHARE-Cog is a new, short cognitive screening instrument developed and validated to assess cognition in the SHARE. In this cross-sectional analysis, it has good-excellent diagnostic accuracy for identifying cognitive impairment in this wave of SHARE, but further study is required to confirm this in previous waves and over time.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".