Bibliographic record
Abstract
Abstract Importance This study validates the use of a telephone version of the MoCA Test, which helps health professionals assess patients remotely. Objectives This is a validation study for a 5 minute telephone version of the MoCA. Study design The subjects’ performances were compared to results obtained from the full version of the MoCA test, at an average of 50.8 days, prior to having completed the 5 Min Telephone MoCA. Participants 84 consecutive subjects were approached to participate in the study. Subjects had to be over 50, male or female, an education level of 6 years. Settings Subjects were recruited in a memory clinic setting. Outcomes and Measures Correct classification and correlation between the Telephone MoCA and full MoCA was evaluated, with area under the curve (AUC) analysis, and Pearson Coefficient respectively. Results 81 subjects were recruited, and 3 subjects declined participation. The average age of the subjects was 69 (Range 47‐92) with a sex distribution of 42 females and 41 males. The average education level for the subjects was 12.6 years. The 5 Min Telephone MoCA results correlated well with the full version of the MoCA, with an area under the curve of 0.864 and a strong Pearson Coefficient of 0.74. Conclusion and Relevance The 5 Min Telephone MoCA is feasible, practical, and reliable, when compared to the full, in‐clinic, MoCA. Clinicians are already familiar with the use of the full MoCA Test, and this new 5 min telephone version, can help them reliably assess patients who cannot make to an in clinic evaluation.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".