How One eConsult Service Is Addressing Emerging COVID-19 Questions
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has made innovative solutions to providing safe, effective care paramount. eConsult allows primary care providers to access specialist advice for their patients without necessitating an in-person visit. This study aims to explain how an eConsult service adapted to providing care for COVID-19 patients and examine its impact on patient care. METHODS: We conducted a cross-sectional analysis of cases submitted to COVID-19 specialties through the Ontario eConsult service between October 2020 and April 2021. Utilization data were extracted from all eligible cases to assess number of cases submitted, patterns of use, response times, and case outcomes (ie, whether eConsult resulted in new or additional information, whether or not a referral was needed). RESULTS: 2783 eConsults were submitted to 5 COVID-19 specialty groups during the study period. 71% of the cases were for vaccine-related questions. The median response interval was 12 hours. Providers received advice for a new or additional course of action in 36% of cases. 84% of the cases did not require a referral. CONCLUSIONS: Our study demonstrated the effectiveness of rapidly adapting eConsult for COVID-19 care and supports similar action for other services.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".