Quality of life and <scp>cost‐effectiveness</scp> of convalescent plasma compared to standard care for hospitalized <scp>COVID</scp>‐19 patients in the <scp>CONCOR</scp>‐1 trial
Bibliographic record
Abstract
BACKGROUND: The CONvalescent Plasma for Hospitalized Adults With COVID-19 Respiratory Illness (CONCOR-1) trial was a multicenter randomized controlled trial assessing convalescent plasma in hospitalized COVID-19 patients. This study evaluates the cost-effectiveness of convalescent plasma and its impact on quality-of-life to provide insight into its potential as an alternative treatment in resource-constrained settings. METHODS: Individual patient data on health outcomes and resource utilization from the CONCOR-1 trial were used to conduct the analysis from the Canadian public payer's perspective with a time horizon of 30 days post-randomization. Baseline and 30-day EQ-5D-5L were measured to calculate quality-adjusted survival. All costs are presented in 2021 Canadian dollars. The base case assessed the EQ-5D-5L scores of hospitalized inpatients reporting at both timepoints, and a utility score of 0 was assigned for patients who died within 30 days. Costs for all patients enrolled were used. The sensitivity analysis utilizes EQ-5D-5L scores from the same population but only uses costs from this population. RESULTS: 940 patients were randomized: 627 received CCP and 313 received standard care. The total costs were $28,716 (standard deviation, $25,380) and $24,258 ($22,939) for the convalescent plasma and standard care arms respectively. EQ-5D-5L scores were 0.61 in both arms (p = .85) at baseline. At 30 days, EQ-5D-5L scores were 0.63 and 0.64 for patients in the convalescent plasma and standard care arms, respectively (p = .46). The incremental cost was $4458 and the incremental quality-adjusted life day was -0.078. DISCUSSION: Convalescent plasma was less effective and more costly than standard care in treating hospitalized COVID-19.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".