Comparison of Diagnostic Accuracy of Virtual Assessments vs In-Clinic Assessment in the Foot & Ankle Clinic
Bibliographic record
Abstract
Category: Basic Sciences/Biologics; Other Introduction/Purpose: Remote video consultations between clinicians and patients have been increasing in popularity3, and while there is a universal consensus that virtual assessments play an integral role in delivering health care to remote locations there has not been any longitudinal investigations into the diagnostic accuracy between virtual consultations and that of in-clinic assessments. Accordingly, our study aims to validate the diagnostic acuity of virtual consultation of patients in comparison to in- clinic assessments along with assessing the satisfaction that patients have experienced with their virtual assessments. Methods: The Foot & Ankle clinic at Toronto Western Hospital prospectively collected data via virtual video assessment of patients from March 2020 till April 2021. We retrospectively analyzed the diagnoses of virtual assessments vs. in-clinic assessments and whether there was a change in diagnosis from these visits. 582 patients were assessed via virtual video consultations of which we used the University Health Network Electronic Patient Records to compare the diagnoses documented in the consultation notes done virtually & the in-clinic notes. Along with this the satisfaction scores were populated for the 192 patients who answered the questionnaire. Satisfaction scores were weighted 0-5, of which 0 represented 'Not At All' and 5 represented 'Definitely'. Results: Analysis of the 582 virtual video consults resulted in 30% of those patients who required in-clinic examination. Assessing these patients, we found a diagnostic accuracy of 95%, with most of the virtual assessments having an appropriate diagnosis. We did nonetheless find that 10% of patients assessed in-clinic demonstrated additional pathology in conjunction with the pathology diagnosed virtually. Assessment of the satisfaction scores demonstrated that 78.6% of patients were satisfied (4/5 & 5/5) with the virtual assessment and 8.34% were not satisfied (1/5 & 0/5). Conclusion: Our study demonstrates that high diagnostic accuracy is attainable through virtual video assessments, thus making it an integral tool for delivery of health care to patients at a time when the COVID-19 pandemic has created significant barriers. Given the constraints placed on our health care systems, this study also demonstrates that patients are satisfied with the virtual assessments conducted through the OTN system. [GT1] Additionally, the implications of such a tool provides benefits to reducing costs to health care systems and to patients who live in remote locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".