Pragmatic Recommendations for the Use of Diagnostic Testing and Prognostic Models in Hospitalized Patients with Severe COVID-19 in Low- and Middle-Income Countries
Bibliographic record
Abstract
Management of patients with severe or critical COVID-19 is mainly modeled after care of patients with severe pneumonia or acute respiratory distress syndrome from other causes. These models are based on evidence that primarily originates from investigations in high-income countries, but it may be impractical to apply these recommendations to resource-restricted settings in low- and middle-income countries (LMICs). We report on a set of pragmatic recommendations for microbiology and laboratory testing, imaging, and the use of diagnostic and prognostic models in patients with severe COVID-19 in LMICs. For diagnostic testing, where reverse transcription-PCR (RT-PCR) testing is available and affordable, we recommend using RT-PCR of the upper or lower respiratory specimens and suggest using lower respiratory samples for patients suspected of having COVID-19 but have negative RT-PCR results for upper respiratory tract samples. We recommend that a positive RT-PCR from any anatomical source be considered confirmatory for SARS-CoV-2 infection, but, because false-negative testing can occur, recommend that a negative RT-PCR does not definitively rule out active infection if the patient has high suspicion for COVID-19. We suggest against using serologic assays for the detection of active or past SARS-CoV-2 infection, until there is better evidence for its usefulness. Where available, we recommend the use of point-of-care antigen-detecting rapid diagnostic testing for SARS-CoV-2 infection as an alternative to RT-PCR, only if strict quality control measures are guaranteed. For laboratory testing, we recommend a baseline white blood cell differential platelet count and hemoglobin, creatinine, and liver function tests and suggest a baseline C-reactive protein, lactate dehydrogenase, troponin, prothrombin time (or other coagulation test), and D-dimer, where such testing capabilities are available. For imaging, where availability of standard thoracic imaging is limited, we suggest using lung ultrasound to identify patients with possible COVID-19, but recommend against its use to exclude COVID-19. We suggest using lung ultrasound in combination with clinical parameters to monitor progress of the disease and responses to therapy in COVID-19 patients. We currently suggest against using diagnostic and prognostic models as these models require extensive laboratory testing and imaging, which often are limited in LMICs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".