[Clinical characteristics of coronavirus disease 2019 in a single center of Argentina. Retrospective cohort].
Bibliographic record
Abstract
Coronavirus disease (COVID-19) became a priority health problem. The objective was to evaluate the clinical characteristics, evolution and severity of COVID-19 in a third-level hospital, in the province of Buenos Aires, Argentina. We conducted a retrospective cohort of 101 patients with COVID-19 from March 3 to June 21, 2020. The patients were divided according to the presence or absence of pneumonia and the severity of the disease. The median age was 42 years and 53% were women. The most common symptoms were fever 66% and cough 57%. Dyspnea and fever were associated with the presence of pneumonia. The most prevalent comorbidities were: hypertension 22%, obesity 18%, cardiovascular disease 7% and chronic respiratory disease 7%. The presence of any comorbidity and hypertension were more common in severe cases. The most frequent laboratory findings were: lymphopenia 55%, elevated D-dimer 38%, and thrombocytopenia 20%. In severe diseases, the level of C-reactive protein and D-dimer were higher. Twenty six patients had pneumonia and 24% were healthcare workers. For diagnosis, more than one reverse transcriptase polymerase chain reaction (RT-PCR) sample was needed in 24% of cases. A moderate-high value of the Pneumonia Severity Index (PSI) was more prevalent in severe than mild pneumonia (63% vs. 17%, p 0.032). A mortality of 5% was registered (95% CI 1-11%). The clinical characteristics, severity and prognosis were similar to those described worldwide. We highlight a high proportion of healthcare workers were SARS-CoV-2 positive, the false negative rate of the RT-PCR and the usefulness of the PSI to discriminate the severity of pneumonia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".