Telemedicine Algorithm for the Management of Dizzy Patients
Bibliographic record
Abstract
As a result of the COVID-19 pandemic, telemedicine has been thrust to the forefront of health care. Despite its inherent limitations, telemedicine offers many advantages to both patient and physician as an alternative to in-person evaluation of select patients. In the near term, telemedicine allows nonpandemic care to proceed while observing appropriate public health concerns to minimize the spread of pandemic pathogens. Thus, it behooves practitioners to use telemedicine consultations for common otolaryngology complaints. Assessment of the dizzy patient is well-suited to an algorithmic approach that can be adapted to a telemedicine setting. As best practices for telemedicine have yet to be defined, we present herein a practical approach to the history and limited physical examination of the dizzy patient in the telemedicine setting for the general otolaryngologist. Indeed, once the acute crisis has abated, we suspect that this approach will continue to be an effective way to manage dizzy patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".