P.013 Accuracy of clinical assessments with virtual care in outpatient neurological setting
Bibliographic record
Abstract
Background: Virtual neurological assessments were increasingly used and an important viable option during the COVID-19 pandemic. However, the accuracy of such assessments is unknown. Methods: Clinical records were reviewed in a predominant multiple sclerosis outpatient clinic at an academic teaching hospital from March 23 rd 2020 to March 23 rd 2021 during the COVID-19 pandemic. Patients assessed during this period were analyzed with an initial virtual assessment compared to subsequent in person evaluations. Results: 1036 patients were included. 27.8% (n=288) of consultations were video and 72.2% (n=748) telephone. A total of 13.8% (n=143) of virtual consultations revealed clinical disparities, specifically 13.5% (n=39) video and 13.9% (n=104) telephone consultations. Of all the 1036 cases, 2.32% (n=24) patients stated they were stable but significant changes were seen on the exam, changing the clinical impression. 11.5% (n=119) stated they were deteriorating virtually but not confirmed when examined in person, with an alternative explanation found. Conclusions: Virtual assessments were accurate in over 85% of the outpatient neurological cases during the pandemic. However, it should be noted that the in person neurological exam led to a change in clinical opinion in 13.8% of assessments. 2.32% patients described clinical stability, but different clinical management plans resulted when significant exam findings were identified.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".