Cognitive Assessment for Clinicians
Bibliographic record
Abstract
This book provides clinicians with a theoretically motivated guide to the assessment of patients with cognitive complaints. Its main goal is to teach physicians, psychiatrists, and psychologists how to assess cognition in the clinic or at the bedside based around the instrument, the Addenbrooke’s Cognitive Examination (ACE), developed in Cambridge over many years and subsequently refined and modified. The latest version is the ACE-III, which is freely available and has been translated into many languages. The early chapters provide a framework in which aspects of cognition are considered as those with a distributed representation in the brain (such as attention and memory) versus those with more focal representation (such as language, praxis, and spatial abilities). There are descriptions of the major syndromes encountered in clinical practice, notably delirium and dementia, which have been updated to incorporate recent discoveries. There follows the all-important section on history taking and the ‘meat of the book’: how to perform bedside cognitive testing. The ACE-III is contrasted to other commonly used brief standardized mental test schedules (such as the Montreal Cognitive Examination). Sixteen cases with a full range of cognitive disorders illustrate the method recommended. Finally, there is an appendix outlining the range of formal tests commonly used in neuropsychological practice.
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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 | 0.010 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.040 |
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".