Attended With and Head-Turning Sign can be clinical markers of cognitive impairment in older adults
Bibliographic record
Abstract
BACKGROUND: Comprehensive neurocognitive assessment may not be performed in clinical practice, as it takes too much time and requires special training. Development of easily applicable, time-saving, and cost effective screening methods has allowed identifying the individuals that require further evaluation. The aim of present study was to assess the diagnostic value of the Attended With (AW) and Head-Turning Sign (HTS) for screening cognitive impairment (CI). METHODS: Comprehensive geriatric assessment was performed in 529 elderly outpatients, and the presence or absence of AW and HTS was investigated in them all. RESULTS: Of the 529 patients, of whom the mean age was 75.67 ± 8.29 years, 126 patients were considered as CI (102 dementia, 24 mild CI). The patients with positive AW had significantly lower scores on Mini-Mental State Examination, Cognitive State Test, and Montreal Cognitive Assessment, and activities of daily living compared to AW (-) patients (p < 0.001). Similar significant findings were obtained in the patients with positive and negative HTS (p < 0.001). The sensitivity, specificity, positive predictive value, and negative predictive value of AW in detecting CI were 92%, 37%, 31.4%, and 93.7%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of HTS were 80%, 64%, 41.8%, and 91.5%, respectively. The area under the receiver-operating characteristics curve was 0.90 for AW and 0.82 for HTS. CONCLUSION: AW and HTS are fast, simple, effective, and sensitive methods for detecting CI. Therefore, they can be used for older adults attending the primary care settings with memory loss. Those with positive AW or HTS can be referred to the relevant centers for detailed cognitive assessment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".