Trends in Use of IV Vitamin C Among Patients With Sepsis
Bibliographic record
Abstract
OBJECTIVES: We sought to determine trends in use of IV vitamin C for hospitalized patients with sepsis in the context of evolving evidence, including a single-center before-after study in late 2016 and several trials in 2019-2021. DESIGN: Retrospective cohort study. SETTING: One thousand one hundred fifteen U.S. hospitals contributing to the Premier Healthcare Database, 2008-2021. PATIENTS: Eleven million three hundred seventy-five thousand three hundred twenty-six adult inpatients with sepsis. INTERVENTIONS: IV vitamin C, at any point of the hospital stay. MEASUREMENTS AND MAIN RESULTS: Patients had a median (interquartile range [IQR]) age of 71 years (59-81 yr) and a median (IQR) of 5 comorbidities (4-7 comorbidities); 53.0% were female; on hospital day 1, 6.9% were mechanically ventilated and 7.5% received a vasopressor. Overall, 32,131 patients (0.3%) received IV vitamin C at any point during hospitalization. During the study period, administration fell from 2008, quarter 1 (0.5%) through 2017, quarter 1 (< 0.1%), then rose and peaked in 2020, quarter 1 (0.6%), and fell through 2021, quarter 4 (0.1%). Examining three time periods defined by predetermined cutpoints (2015 quarter 4, when International Classification of Diseases coding for sepsis changed, and 2020 quarter 1, when the COVID-19 pandemic began), vitamin C use also varied ( p < 0.001): 0.2% (2008 quarter 1 to 2015 quarter 3); 0.3% (2015 quarter 4 to 2019 quarter 4); and 0.3% (2020-2021). Temporal trends were similar in sicker subcohorts defined by early mechanical ventilation, early vasopressor use, and diagnosis of COVID-19 (2020-2021). A multilevel logistic regression model with data from 91 hospitals that contributed at least 1 sepsis case per quarter showed a similar utilization pattern, with substantial between-hospital variability (median odds ratio, 7.78; 95% CI, 5.45-11.58). CONCLUSIONS: IV vitamin C prescription for hospitalized patients with sepsis in the United States was overall infrequent over the 14-year study period, rising after the publication of a before-after study and declining in the COVID-19 pandemic as clinical trial results emerged.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".