Supplementary Material for: Development of the Story Telling Examination for Early Mild Cognitive Impairment (Pre-Mild Cognitive Impairment) Screening
Bibliographic record
Abstract
Introduction: Cognitive function prior to mild cognitive impairment (MCI) has become a burgeoning interest. Tools used to detect this early period before MCI are being pilot-tested. This study aimed to develop a new test to detect pre-MCI and to examine its content validity and feasibility. Methods: The Story Telling Examination for Early MCI Screening (STEEMS), an audio cognitive test, was developed. It covers ten cognitive domains, e.g., executive function, language fluency, abstract reasoning. Face and content validity were examined by experts in geriatric psychiatry and psychology. The content validity index was 1.00. STEEMS comprised 12 items with 2–4 types of scoring. The tool was further examined in 16 pilot samples for feasibility among healthy participants having no cognitive impairment (Montreal Cognitive Assessment [MoCA] test score ≥25, Mini-Cog ≥3) and no depressive symptoms (Geriatric Depression Scale <6). Results: The 16 healthy older individuals aged 59–73 years, mean age was 65.06 ± 4.07 years, were predominantly males (68.8%). STEEMS scores ranged from 10 to 25, with a mean of 18.38 (SD = 4.2). Thirteen percent obtained 100% correct on the STEEMS, 63% scored 68–92% correct, and 25% scored 40–60% correct. The pre-MCI scores are illustrated by a bell curve’s graphical depiction, suggesting a normal distribution probability distribution. Correlation between STEEMS and MoCA test scores was observed. STEEMS showed to be feasible for early elderly or late adults as being brief and easy to understand. The time spent to administer was predictably less than 7 min. Discussion/Conclusion: STEEMS could potentially serve as a tool for pre-MCI screening. Further study and investigation in a larger population are required.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.711 | 0.260 |
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".