Prognostic Value of Inflammatory Markers in Hospitalized COVID-19 Patients
Bibliographic record
Abstract
It is unclear whether D-dimer is a disease-specific marker for COVID-19 or part of the general inflammatory response alongside C-reactive protein (CRP) and other acute-phase reactants. We extracted data of patients hospitalized with COVID-19 for demographics, comorbidities, biochemical data, and outcomes. Using multivariable logistic regression, the value of D-dimer in predicting intensive care unit (ICU) admission or mortality was measured. Of 1175 patients, 263 were admitted to the ICU and 226 died. CRP predicted both ICU admission and mortality [Odds ratios (ORs) with 95% confidence interval 1.01 (1.01–1.01) and 1.00 (1.00–1.01), respectively] but D-dimer was not predictive of either outcome [ORs 1.02 (0.97–1.06) and 0.99 (0.93–1.06)]. This suggests D-dimer levels are not independently predictive of COVID-19 severity or mortality. Our results confirm findings from smaller cohorts and demonstrate the inflammatory characteristics of COVID in the Canadian context.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".