Vitamin C Does Not Affect Platelet Counts in Patients With Sepsis: A Post hoc Analysis of the Lessening Organ Dysfunction With Vitamin C Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: Vitamin C has been linked to alterations in platelet count and aggregation behavior. Given recent findings suggesting an association between vitamin C and adverse outcomes in patients with septic shock, we aimed to investigate whether vitamin C influences mortality in septic patients through its impact on platelets. DESIGN: Post hoc analysis of the Lessening Organ Dysfunction With Vitamin C (LOVIT) randomized trial (clinicaltrials.gov NCT03680274). SETTING: Multicenter international study. PATIENTS: Patients were included with an ICU stay of more than 24 hours, confirmed or suspected infection, vasopressor requirement, and availability of platelet count data. INTERVENTION: Vitamin C (50 mg/kg body weight) every 6 hours for 4 days, or placebo. MEASUREMENTS AND MAIN RESULTS: Of the 863 patients enrolled in the LOVIT trial, 859 had available platelet count data at any time. Although the longitudinal trajectory of platelet count was significantly associated with 28-day mortality (hazard ratio 0.97 per 10 × 109/L increase, 95% CI, 0.96–0.98), there was no interaction between the effect of vitamin C on mortality and either platelet count at baseline or over time. CONCLUSIONS: These results do not support the hypothesis that vitamin C administration increases mortality risk by affecting platelet count.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".