P1‐279: THE ROLE OF NEUROPSYCHIATRIC SYMPTOM IN PREDICTING THE CONVERSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by cognitive decline and appearance of neuropsychiatric symptoms (NPS). Appearance of NPS and decline in working and episodic memory have been considered as a significant risk factor for MCI progression to AD. We propose to analyze NPI (total score and sub scores) and MMSE over 6 years to compare scores between MCI-converters (MCI-C) and MCI-non converters (MCI-NC). All statistical analyses were performed with analyses of variance, Hochberg's GT2, linear correlation and regression using SPSS 25. We selected 150 subjects from ADNI phase 3. Subjects were divided into two groups: 36 MCI-C and 114 MCI-NC. We compared the two groups at 12-month intervals for 6 years. NPI subdomains were counted as percentage to evaluate the amount of symptom increase between baseline and the last available score. To analyze MMSE score, we grouped patients according to their NPI score into three groups: NPI negative (N=55), NPI low (NPI score= 1-9, N=65), and NPI high (NPI score=10 and above, N=30). The NPI total and MMSE scores at 12-month intervals for the duration of 6 years were significantly different between MCI-C and MCI-NC (p<0.05) except at baseline. In the MCI-C group, between baseline and end of the study, the apathy domain increased the most (22%) and irritability/lability increase the least (0%). In MCI-NC, agitation/aggression (21.5%) increased the most and aberrant motor behaviour decreased the most (-14.04%) (Table 1). For the three NPI group, MMSE scores were significantly different between them (p<0.05). Post-hoc analyses revealed that NPI negative and NPI high group were significantly different (p<0.05). There was a significant negative correlation (Figure1-3) in month 36, 48, and 72 between MMSE and NPI (p<0.05) and significant linear regression for those timeframes. Based on those equation, a one-point increase in NPI total score will lead to a 0.10−0.30 decrease in MMSE score in the MCI-C group.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".