Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA), developed by Nasreddine et al., (2005) , is an interactive tool developed to detect mild cognitive dysfunction through the assessment of the cognitive domains of attention, concentration, executive function, memory, language, visuoconstructional skills, conceptual thinking, calculations, and orientation. The MoCA comprises 11 items and example items include drawing a line from a number to a letter in ascending order, copying a drawing of a cube as accurately as possible, and drawing a clock with hands at ten past eleven (11:10). The MoCA is similar to the Mini Mental State (MMSE; see Chapter 27 ) although it is somewhat more complex as it places greater emphasis on frontal executive function and attention tasks. Thus it is considered more sensitive in detecting mild cognitive impairment (MCI) as well as non-Alzheimer’s neurocognitive disorder (NCD) when compared with the gold standard MMSE ( Wong et al., 2013 ). The MoCA takes approximately 10 minutes to administer and the maximum possible score is 30. Higher scores are indicative of better executive functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".