A community‐based study of reporting demographic and clinical information concordance between informant and cognitively impaired participants
Bibliographic record
Abstract
Abstract Background We studied concordance between informants’ and cognitively impaired participants’ reporting of demographic and clinical information in a community‐based cohort study. Method As part of the community‐based Brain Attack Surveillance in Corpus Christi‐Cognitive (BASIC‐C) project, households in Nueces County, Texas, USA, were randomly identified. Participants ≥ age 65 were recruited using door‐to‐door (5/1/2018‐3/15/2020) and phone (04/20/2020‐current) recruitment. Individuals with possible cognitive impairment were identified using the Montreal Cognitive Assessment (MoCA) during door‐to‐door recruitment and Telephone Montreal Cognitive Assessment (T‐MoCA) during phone recruitment. Participants who scored ≤25 and ≤18 on the MoCA and T‐MoCA respectively were eligible for participation. Named informants and participants both answered questions regarding the participant’s demographics and health status. Models were generated to examine the predictors that influence concordance between informant and participant answers. Predictors in the model included participants’ age, gender, ethnicity, degree of cognitive function (estimated by MoCA/T‐MoCA score), and relationship to informant. Result Table 1 provides the concordance between participant and informant answers. Overall concordance was high. Female participants were two and a half times more likely to have concordant answers about date of birth with informants than non‐spouses and male participants, and spouses were three and a half times more likely to agree with participants on date of birth than non‐spouses. (Table 2). Degree of cognitive function of the study participant also showed a large effect on concordance based on the question of whether the participant was diagnosed with dementia (p < .001) (Table 4). Other relationships in comparison to child, including other family member and friend/neighbor/other, were associated with concordance of answers to participant educational attainment and high blood pressure diagnosis (Table 3 and 5). Questions about participant diagnosis of diabetes, heart disease, stroke, and alcohol consumption, had no significant predictors. Ethnicity was not a significant predictor for any question. Conclusion Though most of our concordance rates were >80%, in studies that ask demographic and health history questions of cognitively impaired participants, the gold standard for “true” information remains uncertain. Our results, along with future research, may indicate that in these scenarios, studies should consider supplementing participant responses with informant contributions.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".