Characterizing telemedicine use in clinical immunology and allergy in Canada before the COVID-19 pandemic
Bibliographic record
Abstract
RATIONALE: There exists a geographic barrier to access CIA care for patients who live in rural communities; telemedicine may bridge this gap in care. Herein we characterized the use of telemedicine in CIA at a population-based level and single centre. METHODS: Before the COVID-19 pandemic, telemedicine care was provided via the Ontario Telemedicine Network (OTN) in Ontario, Canada. Descriptive data were collected from the OTN administrative database and from electronic medical records at a single academic centre during 2014 to 2019. The potential distance travelled and time saved by telemedicine visits were calculated using postal codes. RESULTS: A total of 1298 telemedicine visits was conducted over OTN, with an average of 216 visits per year. Only 11% of the allergists/immunologists used telemedicine to provide care before the COVID-19 pandemic. In the single centre that provided the majority of the telemedicine care, 66% patients were female and the overall mean age was 46. The most common diagnosis was immunodeficiency (40%), followed by asthma (13%) and urticaria (11%). Most patients required at least one follow-up via telemedicine. The average potential two-way distance travelled per visit was 718 km and the average potential time travelled in total was 6.6 h. CONCLUSION: Telemedicine was not widely used by allergists/immunologists in Ontario, Canada before the COVID-19 pandemic. It could offer a unique opportunity to connect patients who live in remote communities and allergists/immunologists who practice in urban centres in Canada. Independent of the current pandemic, our study further highlights the need for more physicians to adopt and continue telemedicine use as well as for healthcare agencies to support its use as a strategic priority once the pandemic is over.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".