Exploring the Utility of Virtual Clinics for Neurosurgical Patient Consults: Cohort Study to Assess Feasibility
Bibliographic record
Abstract
Background: The popularity of virtual clinics has increased in many settings, especially during and following the COVID-19 pandemic. However, their applicability in neurosurgical care remains understudied. Objective: The primary goal of this study was to assess the feasibility of conducting a larger, more definitive study at our hospital site in the future. We assessed participant enrollment rate, ability to complete a neurosurgical consult virtually, need for a third party to be present, and participant satisfaction rates. Preliminary evidence on the utility of virtual examination substitutes, compared to currently used in-person assessments, was also explored in our sample of neurosurgical patients. Methods: In this feasibility study, a cohort of neurosurgery patients, consisting of both new referrals and follow-up visits, was evaluated. Each patient participated in a virtual neurological assessment via Zoom and subsequently in an in-person assessment. Both visits were completed by the same physician. We compared clinical findings and treatment decisions (surgical vs conservative management) between the 2 settings and recorded patient satisfaction with the virtual consultation. Results: A total of 95 patients were deemed eligible for the study, and of the 52 patients contacted, 35 provided verbal consent and were enrolled. Both the virtual and in-person assessments were completed by 30 participants (86%) with an average length of 3.25 days between visits, which was within the required 2-week period outlined in the study protocol. No barriers were noted from participants (n=6; 20%) who required a third party to be present and this individual was present at both visits. Participant satisfaction rate with the virtual consults exceeded 90%. Clinical decisions were consistent between both visits in 28 cases, and in the 2 visits where decisions differed, it was noted to be a result of inconclusive findings during the virtual consultation. Comparison of individual examination components between the virtual and in-person consultations revealed exam findings to be consistent 77% of the time, and importantly, none of these discrepancies led to a change in clinical decision. No single examination component was noted to be inconsistent more than twice. Conclusions: These findings support the applicability of the proposed study design to a larger-scale project. No major obstacles or methodological challenges were encountered in achieving the goals of this feasibility study within the target timeframe. This study provides preliminary evidence to support further exploration of the use of virtual consults to help inform clinical decisions in a neurosurgical population.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".