Importance of public‐private partnerships for nutrition support research: An ASPEN Position Paper
Bibliographic record
Abstract
Parenteral and enteral nutrition support are key components of care for various medical and physiological conditions in infants, children, and adults. Nutrition support practices have advanced over time, driven by the goals of safe and sufficient delivery of needed nutrients and improved patient outcomes. These advances have been, and continue to be, dependent on research and development studies. Such studies address aspects of enteral and parenteral nutrition support: formulations, delivery devices, health outcomes, cost-effectiveness, and related metabolism. The studies are supported by public funding from the government and by private funding from foundations and from the nutrition support industry. To build public trust in nutrition support research findings, it is important to underscore ethical research conduct and reporting of results for all studies, including those with industry sponsors. In 2019, American Society for Parenteral and Enteral Nutrition's (ASPEN's) Board of Directors established a task force to ensure integrity in nutrition support research that is done as collaborative partnerships between the public (government and individuals) and private groups (foundations, academia, and industry). In this ASPEN Position Paper, the Task Force presents principles of ethical research to guide administrators, researchers, and funders. The Task Force identifies ways to curtail bias and to minimize actual or perceived conflict of interests, as related to funding sources and research conduct. Notably, this paper includes a Position Statement to describe the Task Force's guidance on Public-Private Partnerships for research and funding. This paper has been approved by the ASPEN Board of Directors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.209 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.060 | 0.030 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.069 | 0.050 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".