Learning from Adversity: Lessons from the COVID-19 Crisis
Bibliographic record
Abstract
One month into the crisis caused by COVID-19 (coronavirus disease 2019; the disease caused by SARS-CoV-2) in the United States, rheumatologists began to feel the effects of our hastily constructed temporary response plans. While we may not have been staffing the front lines with our colleagues in emergency medicine and critical care, our patients were uniquely vulnerable to infection and continued to require care. Managing their illnesses remained no less important then than it ever was. Necessity forced us to learn important lessons about delivering this care in safer, more efficient ways. While some of these adaptations will revert to “business as usual,” others may change the way we communicate with patients and colleagues long after this crisis has passed. In our own clinic, the importance of social distancing catalyzed a transition to telemedicine. Healthcare has typically been excluded from stay-at-home orders, so patients can still come into the clinic, but should they? Many of us practice in large medical complexes. Reducing traffic in these locations makes social and medical sense. Even for rheumatologists with independent offices, waiting rooms are a worrisome potential locus for virus transmission. Most importantly, physicians and medical staff have close personal contact with dozens of patients per day, many of whom could be asymptomatic carriers. The rationale for moving to telemedicine was strong. Telemedicine is not new to rheumatology. Publications on telemedicine approaches go back 20 years, though the major focus has typically been on the use of these tools to deliver specialty care to rural areas1,2. Despite this, adoption has been slow, and in many places, nonexistent. Many factors have … Address correspondence to Dr. E.M. Ruderman, Northwestern University Feinberg School of Medicine, Division of Rheumatology, 675 North St. Clair 14-100, Chicago, Illinois 60611, USA. E-mail: e-ruderman{at}northwestern.edu
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 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".