Certified Examination Assistants in the Age of Telemedicine: A Blueprint Through Neurology
Bibliographic record
Abstract
The optimal approach to a clinical physical examination via telemedicine is still being explored. The medical community has no standardized or widely followed criteria for telemedicine examinations, so a broad spectrum of approaches is used. Unfortunately, the need for telemedicine is outpacing physical examination validation research. Given that certain specialties have been using telemedicine longer than others, lessons from those specialties might aid in developing standardized protocols for telemedicine. Neurology has been at the forefront of telemedicine use, initially through stroke care and later in multiple subspecialties. We present a framework for optimizing the history taking and physical examination process via telemedicine based on our experience in neurology. This mainly includes remotely examining a patient unassisted or with an untrained assistant present on the patient side of the connection. We also discuss the need for trained, certified assistants to assist the off-site physician in history taking and physical examination. These certified assistants would be allied health professionals who perform high-quality cued patient examinations under direct physician supervision with no responsibility to diagnose or treat. This contrasts with the approach seen in advanced practice providers such as physician assistants and nurse practitioners who undergo years of training to diagnose and treat patients under supervision. This training process would serve as a stepping stone for the development of dedicated certification programs for neurology and other medical specialties; however, assessments of practical training, costs, implementation, and longitudinal quality are warranted.
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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 | 0.033 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".