Analyzing the Usage of the GIM-COVID-19 Long-Term Sequelae Rapid Access to Consultative Expertise (RACE) Line
Bibliographic record
Abstract
Abstract Real time access to guidance for physicians has been offered through Rapid Access to Consultative Expertise (RACE) in British Columbia (BC) for the past 12 years. 1 In the context of the novel coronavirus (COVID-19), the service for RACE was expanded to include a Long-COVID RACE line. 1 We report here the types and frequencies of questions asked to General Internal Medicine (GIM) experts in Long-COVID, by general practitioners in BC. 149 calls over an 11-month period were tracked by GIM experts, and analysis of the call themes was undertaken. These calls mainly involved consults regarding the post-infection COVID-19 symptoms being experienced by Primary Care Practitioners’ patients. Respiratory symptoms were the leading type of symptoms reported, with shortness of breath, cough, fatigue, and fevers being the most common, respectively. This data will be used to inform future resource utilization and provide insights on the usage of the Long-COVID RACE line.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".