Risk Assessment of Hospitalized Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)–Infected Patients Using Laboratory Data and Immune Cell Morphologic Assessment
Bibliographic record
Abstract
CONTEXT.—: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious agent, with the propensity to cause severe illness. While vaccine uptake has been increasing in recent months, many regions remain at risk of significant coronavirus disease 19 (COVID-19)-related health care burden. Health systems will continue to benefit from the availability of a variety of clinical and laboratory models when other triaging models are equivocal. OBJECTIVE.—: To validate previously reported clinical laboratory abnormalities seen in COVID-19 patients and identify what laboratory parameters might be outcome predictive. DESIGN.—: We undertook an observational study of hospital-admitted COVID-19 patients (n = 113), looking at a broad selection of clinical, laboratory, peripheral blood smear, and outcome data during discrete discovery and validation periods from March 2020 to November 2020. RESULTS.—: We confirmed the findings of previous studies noting derangement of a variety of laboratory parameters in COVID-19 patients, including peripheral blood morphologic changes. We also devised a simple-to-use decision tree by which patients could be risk stratified on the basis of red blood cell count, creatinine, urea, and atypical plasmacytoid lymphocyte ("covidocyte") count. This outcome classifier performed comparably to the World Health Organization clinical classifier and the neutrophil-lymphocyte ratio. CONCLUSIONS.—: Our data add to the increasing number of studies cataloguing laboratory changes in COVID-19 and support the clinical utility of incorporating blood morphologic assessment in the workup of hospitalized COVID-19 patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".