Mild cognitive impairment and the uncertainties of diagnosis : reviewing the accuracy of the Montreal Cognitive Assessment and exploring the process of psychosocial adjustment
Bibliographic record
Abstract
Background: Mild Cognitive Impairment (MCI) is a clinical construct reputed to represent an intermediate stage on a continuum between normal aging and cognitive decline. Conceptual and prognostic ambiguity can lead to significant diagnostic challenges and there is a need for accurate screening tests which can assist clinicians with decision-making. A diagnosis of MCI is also associated with considerable uncertainty for patients who may be adjusting to cognitive difficulties along with an increased risk of developing dementia. Beliefs about MCI may influence psychosocial adjustment, and individual differences in ‘psychological flexibility (PF)’, as conceptualised by the Acceptance and Commitment Therapy (ACT) model, may also be involved in this process. Objectives: In order to evaluate the accuracy and clinical utility of a recently developed screening tool for MCI, the Montreal Cognitive Assessment (MoCA), a systematic review of validation and diagnostic test accuracy (DTA) studies for this measure was conducted. Psychosocial adjustment to a diagnosis of MCI was also a key focus. An empirical study was therefore carried out with the aim of evaluating the possible relationships between cognitive impairment, illness representations about MCI, psychological wellbeing and quality of life (QoL), and to assess the potential involvement of PF. Method: Following a systematic search of relevant electronic databases and reference lists, validation and DTA studies of the MoCA were identified and evaluated for methodological quality. For the empirical study, patients recently diagnosed with MCI were recruited from local NHS memory clinic services and completed the MoCA and a questionnaire pack assessing illness representations, PF, mood, anxiety and QoL. Results: The systematic review identified 18 validation and DTA studies. Few of the studies achieved high ratings for methodological quality and problems with representativeness and generalisability were identified. Nevertheless, sensitivity levels appeared robust across studies, though specificity was variable. For the present empirical study, participants reported a spectrum of positive and negative beliefs about MCI. Distress attributed to MCI was associated with anxiety, along with perceptions of more serious illness consequences, while higher PF was associated with higher perceived QoL and mood. Lived experience of MCI appeared to have more relevance to psychosocial adjustment than objective cognitive impairment. Conclusions: The results of the systematic review indicate that while the MoCA is a robust tool overall in the identification of cognitive impairment, estimates of accuracy may be exaggerated by inter-study variation and bias. More rigorous validation studies are therefore needed. Implications for clinical decision-making regarding MCI are discussed and recommendations for future accuracy studies are outlined. The empirical study supported the findings of previous studies of the relevance of illness representations to psychosocial adjustment in MCI and added to the evidence base by providing preliminary support for the possible involvement of PF. The results suggest that both cognitive content and PF may represent possible vehicles for therapeutic change in patients with adjustment difficulties, and indicate that further investigation of these factors is warranted. Conclusions are limited, however, by small sample size and low statistical power. Replication of these findings with a larger and more representative sample is therefore recommended.
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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 | 0.110 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".