Telemedicine Applications for the Evaluation of Patients with Non-Acute Headache: A Narrative Review
Bibliographic record
Abstract
The COVID-19 pandemic has spurred a hasty transition to virtual care but also an abundance of new literature highlighting telehealth's capabilities and limitations for various healthcare applications. In this review, we aim to narrate the current state of the literature on telehealth applied to migraine care. First, telemedicine in the context of non-acute headache management has been shown to produce non-inferior patient outcomes when compared to traditional face-to-face appointments. The assignment of patients to telehealth appointments should be made after referring more urgent cases to dedicated in-person clinics. During the virtual appointment, physicians can ask their patients about the "3 F's" in order to perform a thorough assessment of their headaches: frequency of headache days, frequency of acute medication usage and functional impairment. Clinical assessment scores that have been studied and deemed feasible for telemedicine, safe and efficient include the HIT-6, VAS and MIDAS scores. Although MIDAS was found to be redundant and inadequate to use on a daily basis, we suggest that it can be useful in periodic remote follow-up appointments. Additionally, several mobile health apps have been studied including Migraine Buddy, Migraine Coach and Migraine Monitor. All of these are appropriate for use in telemedicine when combined with an adequate trial period with Migraine Buddy being rated the highest, as it captures the most detailed clinical picture. High satisfaction rates have been reported for virtual headache management which were shown to be equal to in-person consults. These are based on patients' perceived increase in convenience due to avoided travel time, less disruption of their daily routine and feeling more comfortable in the environment of their choice. Despite this, limitations such as technological knowledge, access to videoconferencing modalities and having a more impersonal consultation with the physician may hinder some patients from adopting this service.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".