Reliability of the MMSE Administered In-Person and by Telehealth
Bibliographic record
Abstract
BACKGROUND: Recent advances in telehealth have improved access to health care for those in rural areas. It is important that examinations conducted via telehealth are comparable to in-person testing. A rural and remote memory clinic in Saskatoon provided an opportunity to compare scores on the Mini-Mental State Examination (MMSE) administered in-person and via telehealth. METHODS: After an initial one day assessment in Saskatoon, patients were seen in follow-up at 6 and 12 weeks. Individual patients were randomly assigned to either in-person follow-up assessment in Saskatoon or telehealth assessment in their home community. Patients who initially received in-person assessments were seen by telehealth for their next follow-up visit and vice-versa. The same neurologist administered MMSEs at all visits. The first 71 patients with both 6 and 12 week follow-up assessments were included in this study. The scores of in-person and telehealth MMSE administrations were compared using the methods of Bland and Altman as well as a paired t-test. RESULTS: MMSE scores did not differ significantly between telehealth (22.34 +/- 6.35) and in-person (22.70 +/- 6.51) assessments. CONCLUSION: Telehealth provides an acceptable means of assessing mental status of patients in remote areas.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".