Title thesis: *\tDetecting differences between MCI patients with a psychiatric diagnosis and patients with a neurodegenerative diagnosis using The Montreal Cognitive Assessment
Bibliographic record
Abstract
Abstract:\tBackground. Elderly with mild cognitive complaints are often given a neurodegenerative diagnosis, although these complaints can also belong to a psychological diagnosis. When elderly are wrongly diagnosed with a neurodegenerative diagnosis this causes unnecessary agitation and uncertainty for their future. Use of the Montreal Cognitive Assessment (MoCa) in the elderly psychiatric department can lead to better treatments, especially when the MoCa can distinguish patients with a psychiatric diagnosis from patients with a neurodegenerative diagnosis. Method. The MoCa, a 10-minute cognitive screening tool to assist physicians in detection of mild cognitive impairment (MCI), was used to search for differences between two MCI patient groups (MCI patients with either a psychiatric or a neurodegenerative diagnosis). The two MCI groups were recruited from the elderly psychiatric department Altrecht, Leidsche Rijn Utrecht. With a longitudinal design, the two MCI groups were followed over time. Patients had a first MoCa test (2008-2017) and a second MoCa test (2008-2018), which could then be compared on overall score and on individual domains of the MoCa. Results. No significant differences were found in the two groups on the first MoCa score. Furthermore, both MCI groups scored lower on their second MoCa test compared to their first MoCa test, and the MCI-neurodegenerative diagnosis group scored significantly lower on both time points compared to the MCI- psychiatric diagnosis group. No interaction effect was found between time and the two MCI groups. Only on the domain ‘Orientation’ a significant difference was found between the two MCI groups. Conclusion. Further research with a larger research group and a control group is needed to draw a better conclusion about the differences between the two MCI groups on the MoCa. This research however, showed that there are (small) differences between the two MCI groups on the MoCa and these findings give a good reason for more research on this subject.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.028 | 0.005 |
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".