Nutrition and Metabolism Research Oral Paper Session Abstracts
Bibliographic record
Abstract
Background: Recent observational studies have found that consumers of home parenteral nutrition (HPN) have profoundly disrupted sleep, especially when cycling HPN overnight.The composition and timing of dietary intake are known to influence sleep through various mechanisms.The aim of this analysis was to interrogate the impact of HPN composition and related characteristics on sleep among adult HPN consumers.Methods: We conducted a secondary analysis of observational data on adult HPN consumers' nutrition and objectively ascertained sleep.Participants were adult (18-80 years) male and non-pregnant females in the US, habitual HPN consumers without major changes to their HPN for at least 3 months, and currently on a nocturnal infusion schedule.Participants self-reported HPN characteristics, namely, volume, calories, macro-and micronutrients, and years of HPN use, in addition to infusion schedules including days per week, start times, hours per day, and rate.We calculated the percentage of calories for each macronutrient and glucose-infusion rate (GIR) using milligrams of dextrose per day, kilograms of body weight, and minutes of infusion.During enrollment, participants wore a wrist actigraph (accelerometer) for up to seven consecutive days.The following sleep measures were ascertained based on a validated algorithm: time in bed, sleep duration, sleep efficacy (SE), and wakeafter-sleep onset (WASO).SE and WASO are measures of sleep quality calculated as the percentage of time one spends asleep while in bed and the time one spends awake during the night after initially going to sleep, respectively.For each pair of nutrition-sleep measures, we calculated Spearman's correlation coefficients and a p-value of <0.05 was considered statistically significant.All analyses were conducted in R (version 2022.07.1;The R Foundation for Statistical Computing).Results: A total of 32 adult participants were included; participants had a mean age and BMI of 53 years and 21.6 kg/m2, were majority female (75%), white (93%), and living with short-bowel syndrome (81%).The median duration of HPN use was 6 years, mean HPN volume was 2,281 mL, and the mean rate of infusion was 222.7 mL per hour.Rate of infusion was negatively correlated with both time in bed (r = -0.46)and sleep duration (r = -0.44).Hours of infusion per day was positively correlated with time in bed (r = 0.41).In addition, GIR was negatively
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.281 | 0.091 |
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".