Impact of nationwide essential trace element shortages: A before‐after, single‐center analysis of hospitalized adults receiving home parenteral nutrition therapy
Bibliographic record
Abstract
BACKGROUND: Recent data on the prevalence of essential trace element (ETE) deficiencies in home parenteral nutrition (HPN) patients are scarce. We investigated whether ETE deficiencies are still an important issue for HPN patients and whether the prevalence of such deficiencies may be influenced by nationwide drug shortages. METHODS: We conducted a single-institution, retrospective analysis from 2006 to 2015 of hospitalized HPN patients who continued PN during and in between hospitalizations. In subgroup analysis, patients were dichotomized as those with HPN duration <1 vs ≥1 year. Zinc (Zn), copper (Cu), and selenium (Se) levels were abstracted for patients over the study period. Prevalence of ETE deficiency was compared using chi-squared test for patients hospitalized during nonshortage vs shortage (2011-2014) periods. RESULTS: Ninety-six patients were included in the analysis. Prevalence of ETE deficiency during nonshortage vs shortage periods was 48% vs 54% (Zn), 15% vs 21% (Cu), and 24% vs 48% (Se; P = .01), respectively. When comparing patients who received HPN <1 year vs ≥1 year, the prevalence of Se deficiency doubled during shortage in both subgroups (24% to 42% vs 26% to 49%); and Cu deficiency tripled during shortage period in the group receiving HPN ≥1 year (5% to 16%). CONCLUSION: ETE deficiency is prevalent in hospitalized HPN patients and was exacerbated during nationwide shortages of parenteral supplements. Statistical significance may be limited by small sample size. Future studies are needed to determine optimal ETE supplementation strategies for minimizing the impacts of nationwide drug shortages on HPN patients.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".