Examining the Use of Cognitive Assessments in Clinical and Healthy Populations: A Focuson Spatial Cognition
Bibliographic record
Abstract
Spatial navigation and orientation deficits are often presented in early stages of Alzheimer’s disease (AD) and can even be recognised in the predementia stage of Mild Cognitive Impairment (MCI). Despite this, specialized tests of spatial cognition are not used in clinical settings as part of MCI/AD screening procedures. Currently, the most widely used cognitive marker for AD diagnosis is episodic memory. Episodic memory decline is evident not only in other forms of dementia but also during healthy ageing. This complicates the early detection of AD which is essential in allowing for early intervention and treatment of the disease. Recent research has focused on spatial navigation/orientation as a potential cognitive marker for MCI and AD and has shown greater specificity in detecting preclinical AD compared to episodic memory. Two widely used clinical screening tools for MCI/AD detection are the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). In Chapter 2, the usefulness of these tests in MCI/AD detection was examined, as well as utility of spatial subscales in predicting AD conversion from MCI. MoCA subscales relating to spatial ability predicted MCI progression to AD and reversion to cognitively normal, highlighting the importance of assessing spatial cognition in these clinical populations. Tests of spatial cognition were used in Chapter 3 with a healthy population to determine their use in a clinical setting as possible follow-up assessments with MCI/AD patients. These tests were deemed useful for examining spatial cognition in a healthy population, although further research would be required in order to inform clinical practice. This thesis displays promising early findings for the use of spatial cognition tests as screening tools for MCI/AD.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".