Dietary nitrate supplementation for preventing and reducing the severity of winter infections, including COVID-19, in care homes (BEET-Winter)- a randomised placebo-controlled feasibility trial
Bibliographic record
Abstract
Abstract BackgroundInfections cause considerable care home morbidity and mortality. Nitric oxide (NO) has broad-spectrum anti-viral, bacterial and yeast activity in vitro. We assessed the feasibility of supplementing dietary nitrate (NO substrate) intake in care home residents.MethodsWe performed a cluster-randomised placebo-controlled trial in UK residential and nursing care home residents and compared nitrate containing (400 mg) versus free (0 mg daily) beetroot juice given for 60 days. Outcomes comprised feasibility of recruitment, adherence, salivary and urinary [nitrate], and ordinal infection/clinical events.ResultsOf 30 targeted care homes in late 2020, 16 expressed interest and only 6 participated. 49 residents were recruited (median 8 [interquartile range 7–12] per home), mean (standard deviation) age 82 (8) years, with proxy consent 41 (84%), advance directive for hospital non-admission 8 (16%) and ≥ 1 doses of COVID-19 vaccine 37 (82%). Background dietary nitrate was < 30% of acceptable daily intake. 34 (76%) residents received > 50% of juice. Residents randomised to nitrate vs placebo had higher urinary nitrate levels, median 50 [18–175] v 18 [10–50] mg/L, difference 25 [0–90]. Data paucity precluded clinical between-group comparisons; the outcome distribution was: no infection 32 (67%), uncomplicated infection 0, infection requiring healthcare support 11 (23%), all-cause hospitalisation 5 (10%), all-cause mortality 0. Urinary tract infections were most common.ConclusionsRecruiting UK care homes during the COVID-19 pandemic was partially successful. Supplemented dietary nitrate was tolerated and elevated urinary [nitrate]. Together, infections, hospitalisations and deaths occurred in 33% of residents over 60 days. A larger trial is now required.RegistrationISRCTN51124684
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".