High-dose vitamin-C induced prolonged factitious hyperglycemia in a peritoneal dialysis patient: a case report
Bibliographic record
Abstract
BACKGROUND: High-dose vitamin C is increasingly used for sepsis and more recently for coronavirus disease 2019 (COVID-19) infections. Proponents argue that the low cost and near perfect safety profile of vitamin C support its early adoption. Yet, adverse events might be underreported and underappreciated. CASE PRESENTATION: We report a 73-year-old non-diabetic white man with end-stage renal disease on peritoneal dialysis admitted to the intensive care unit with septic shock that was suspected to be due to peritonitis. The patient was enrolled in LOVIT (Lessening Organ Dysfunction with VITamin C; ClinicalTrials.gov identifier: NCT03680274), a randomized placebo-controlled trial of high-dose intravenous vitamin C. He developed factitious hyperglycemia, as measured with a point-of-care glucometer, that persisted for 6 days after discontinuation of the study drug, confirmed to be vitamin C after unblinding. He also had short-lived iatrogenic coma because of hypoglycemia secondary to insulin administration. These events triggered a protocol amendment. CONCLUSIONS: Although factitious hyperglycemia has been reported before using certain glucometers in patients treated with high-dose vitamin C, the persistence of this phenomenon for 6 days after the discontinuation of the therapy is a distinguishing feature. This case highlights the importance of monitoring glucose with a core laboratory assay for up to a week in specific populations, such as patients on peritoneal dialysis.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".