Trust me, I’m a validated test!?: Unseen mild (cognitive) impairment and the use of the MoCA in an old age psychiatry setting.
Bibliographic record
Abstract
In clinical practice two seemingly distinct disorder clusters are referred to the old age psychiatry; psychiatric disorders and neurodegenerative disorders. However, both psychiatric and neurodegenerative causes can start with (mild) cognitive deficits. An elaborate neuropsychological assessment is part of the gold standard for identifying the cause. As it is expensive and burdensome for the patient to do this in specialised outpatient clinics, a triaging test before a referral is made is desirable. A fast test with discriminatory power in this group of patients with a non-uniform presentation is of great value as an increasing number of people have dementia; they are examined earlier in the process, and this interferes with regular (psychiatric) treatments and complicates diagnostics due to increasing overlapping symptom presentation. This test should be validated to allow the scores to be interpreted properly so it can help to identify or exclude (mild) cognitive impairment. The Montreal Cognitive Assessment (MoCA) is a short screening test (10 minutes long) for cognitive complaints but it was not validated in old age psychiatry. Not only did we validated the MoCA for this setting, we also show how to improve its use in clinical practice by using a double threshold. In addition to the preceding summary one must consider that patients tend not to mention all of their needs during visits. The symptoms experienced do not always have to correspond to their objective symptoms. This also applies for what close relatives report. Therefore, we must be aware of that patients are not always able to properly draw attention to their request for help or, in fact, the cause of their complaints. Screening could be a solution, but often comes with costs. To screen or not to screen – that is often the question. In this dissertation, we have provided the arguments that the MoCA can play a substantial role colouring the grey area that this question raises. Therefore allowing (part of) this discussion to be settled for cognitive impairment. This accounts especially in old age psychiatry where MCI is a frequent issue due to multiple aetiologies. What you see is not always what you get. We show that this is also true for needs, unmet needs, and the (free) concentration of valproic acid. We think that the MoCA is suitable for MCI screening in old age psychiatry, with its population at risk. However knowing its strengths and weaknesses is essential. It is faster, cheaper, and therefore easier to apply than a neuropsychological assessment; however, it will have difficulties in differentiating the aetiologies, including cognitive impairment of psychiatric origin. Therefore the MoCA should not only be used on indication (triaging) but also to get an indication (screening) in old age psychiatry. If your MMSE score is wrong, then something is really going on. If your MoCA score is right, then you should be alright. If your MoCA score is so so, active monitoring is the way to go. If your MoCA score is low, an elaborate assessment should follow. We show in this dissertation the importance of knowing the strengths and weaknesses of a screening instrument in old age psychiatry. Trust me, I‘m a validated test…….? Trust me, I’m a doctor , and know how to use a validated test!
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".