Development and external validation of a novel dementia risk prediction score in the UK Biobank cohort
Bibliographic record
Abstract
Abstract Background Globally, up to 40% of dementia cases may be prevented if several key risk factors, such as low education and obesity, are targeted. This has motivated interest into the development of risk scores that aim to quantify an individual’s risk of developing dementia within a given time frame. However, translation to a clinical setting has been hampered by either a lack of external validation, poor out‐of‐sample performance, or the integration of measures (e.g., MRI, cognitive testing) that are costly or time intensive to administer. This study aimed to develop a novel dementia risk score suitable for a primary care setting and compare its discrimination accuracy with other risk scores. Method 208,108 UK Biobank (UKB) participants were examined, with cases of incident dementia (mean time to diagnosis ± SD = 5.91 years ± 2.02) identified through self‐report, hospital inpatient records and death certificates (n = 1,270, 0.6%). The dataset was randomly split into a training (80%) and test set (20%). Potential predictors (n = 30) included Apolipoprotein E4 (APOE4) status and cardiovascular, medical and lifestyle factors collected at baseline. Logistic LASSO regression was employed for variable selection and logistic regression was then used to derive beta coefficients for each predictor included in the UKB dementia risk score (UKB‐DRS). The area under the curve (i.e., AUC) was used to compare the discriminative ability of the UKB‐DRS to the CAIDE, ANU‐ADRI, Dementia Risk Score (i.e., DRS) and Framingham risk score, in both the test set and the Whitehall II study (WHIII, n = 2,974), which served as an external dataset. Result The UKB‐DRS consisted of age, sex, education, APOE4 status, a medical history of diabetes, stroke and depression and a family history of dementia. The UKB‐DRS demonstrated good discrimination accuracy in both the test set (AUC [95% ] = 0.79 [0.77, 0.82]) and external dataset (AUC [95% CI] = 0.83 [0.79,0.87]). Additionally, the UKB‐DRS outperformed all other risk scores (the DRS had the next best AUC [95% CI]: UKB = 0.77 [0.74, 0.8]; WHII = 0.77 [0.73, 0.81]). Conclusion This study contributes a novel dementia risk score that may be used to guide primary prevention strategies.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".