Outpatient Management of Oligosymptomatic Patients with Respiratory Infection in the Era of SARS-CoV-2. Experience from Rural German General Practitioners
Bibliographic record
Abstract
Abstract Background: Covid-19 is causing a pandemic and forces physicians to restructure their work. We want to share our experience in the outpatient management of potentially-infected patients with special consideration of altered national test strategies during the crisis.Methods: We analysed patients with respiratory symptoms reporting to our three rural general practitioner (GP) offices in North Rhine-Westphalia, Germany, from 27.01-20.04.2020 (n=489 from a total of 6,090 patients). A history of symptoms was taken at the doorstep following a specific questionnaire. Patients with respiratory symptoms were examined in a separated isolation area, while the others were allowed to enter the office. We applied the first recommended algorithm of the German Robert Koch Institute (RKI) to test suspected patients and compared our results with an adapted, more liberal version of the RKI, which is currently applied in Germany.Results: Eighty patients (16.36%, mean age: 47.03 years+-18.08) were sent to a nasopharyngeal smear. Five patients (6.25%) proved to be positive, four of whom had established risk factors for COVID-19. Overall, the most common symptoms were cough (83.75%), sore throat (71.25%), as well as myalgia and fatigue (66.25%). The most common diagnoses were rhinopharyngitis (37.22%) and acute bronchitis (30.27%). A sore throat was more common in positively-tested patients (80% vs. 12%). Applying the first RKI test strategy yielded 6.25% of positive tests (n=80), while the more liberal later RKI recommendation would have achieved 1.36% positive tests from 369 patients. No positive test was missed by applying the conservative strategy. None of our employees called in sick during this period, which emphasises the efficacy and safety of our screening methods. Conclusion: A clinical distinction between ordinary respiratory infections and COVID-19 is not possible in a low-prevalence population. Our model to prevent unprotected physical contact, screen patients in front of the office with protective equipment, and examine respiratory infections in separated areas works in the GP setting without overt health risks for employees. Thus, this approach should be used as a GP standard to uphold patient care without major health risks for the personnel. Large multi-centre studies are necessary to work out the most suitable test strategy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".