Platelet Counts Variation and Platelet Indices According to the Severity and Outcome of COVID-19
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19), caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is characterized by various biological changes, notably hematological. The value of platelet count and its indices in the evolution of the disease has been raised by several authors. Our objective was to evaluate the variation in blood platelet counts and indices in relation to the progression of COVID-19. Methods: We conducted this retrospective study between May 12, 2020 and March 20, 2021 at the Epidemiological Treatment Center and Hematology Laboratory of Aristide Le Dantec Hospital. Patients who were tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcription-polymerase chain reaction (RT-PCR) and who had undergone at least one complete blood count on admission were included. Excel 2019 and SPSS v.20 were used for data processing. Results: A total of 332 patients were included with a median age of 60 years (12 - 100 years). The male gender was more represented with 58.1%. Forty-nine (49) individuals (14.75%) of the patients included in this study were seriously ill, and 39 (11.75%) of them died. The majority of our 82.8% had normal platelet counts, while only 8.4% had thrombocytopenia. The later was even more frequent in patients with poor prognosis of COVID-19 disease (11.88% versus 7.35%). Platelet indices were significantly higher in the severe group than in the non-severe group. However, only the platelet distribution width (PDW) was significantly increased according to the severity of COVID-19 disease. Conclusion: According to our observations, PDW is an important marker in the risk stratification, the prognosis and unfavorable evolution of COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".