Alcohol Withdrawal: Possible Risk of Latent Scurvy Appearing as Tiredness: A STROBE-Compliant Study
Bibliographic record
Abstract
BACKGROUND: Little is known about the prevalence of vitamin C deficiency in the population of individuals who are withdrawing from alcohol, and about possible consequences of latent scurvy. The aim of this study was to evaluate the prevalence of vitamin C deficiency in patients who were withdrawing from alcohol, its correlation with latent scurvy (mainly tiredness and weakness) and the change in the latter symptoms at 3 months after oral vitamin C supplementation. METHODS: A total of 47 patients (33 males, 14 females) who volunteered to undertake alcohol detoxification were included prospectively between January 2014 and November 2016. Determination of vitamin C blood levels was performed, and selected clinical signs of latent scurvy were recorded in a structured questionnaire. The decrease of tiredness after vitamin C supplementation was also studied 3 months after the inclusion of patients who had no other explanation for their weakness. RESULTS: About 57.44% of the patients were affected by vitamin C deficiency (< 11.4 mol/L). Less than one-third (29.70%) had a normal plasma vitamin C level. There was a clear correlation between decreased vitamin C levels and the presence of tiredness (P = 0.003) and no correlation between gum inflammation or purpura (P = 0.97 and 0.44 respectively). After 3 months of alcohol withdrawal and vitamin C supplementation, 89% of the patients reported a decrease of tiredness. CONCLUSIONS: Vitamin C deficiency is prevalent in patients undertaking alcohol detoxification. Patients with this vitamin deficiency status often suffer from tiredness, which may be symptoms of latent scurvy. Even if tiredness is a highly nonspecific symptom, presence of fatigue should draw attention and the vitamin C level should be determined. Our preliminary data suggest that vitamin C supplementation, may decrease fatigue and improve patient's quality of life. This last evolution needs to be confirmed by a double-blind randomized study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".