Hyperphagia: Current concepts and future directions proceedings of the 2nd international conference on hyperphagia
Bibliographic record
Abstract
OBJECTIVE: Hyperphagia is a central feature of inherited disorders (e.g., Prader-Willi Syndrome) in which obesity is a primary phenotypic component. Hyperphagia may also contribute to obesity as observed in the general population, thus raising the potential importance of common underlying mechanisms and treatments. Substantial gaps in understanding the molecular basis of inherited hyperphagia syndromes are present as are a lack of mechanistic of mechanistic targets that can serve as a basis for pharmacologic and behavioral treatments. DESIGN AND METHODS: International conference with 28 experts, including scientists and caregivers, providing presentations, panel discussions, and debates. RESULTS: The reviewed collective research and clinical experience provides a critical body of new and novel information on hyperphagia at levels ranging from molecular to population. Gaps in understanding and tools needed for additional research were identified. CONCLUSIONS: This report documents the full scope of important topics reviewed at a comprehensive international meeting devoted to the topic of hyperphagia and identifies key areas for future funding and research.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".