Defining Hyperphagia for Improved Diagnosis and Management of MC4R Pathway–Associated Disease: A Roundtable Summary
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hyperphagia is a condition associated with rare obesity-related diseases, presenting as a pathologic, insatiable hunger accompanied by abnormal food-seeking behaviors. In October 2023, a group of researchers and clinicians with expert knowledge on hyperphagia convened at the annual ObesityWeek meeting to discuss the need for a unified definition of hyperphagia and key items necessary to improve the identification, assessment, and treatment of hyperphagia in patients with melanocortin 4 receptor (MC4R) pathway-associated diseases. RECENT FINDINGS: The definition of hyperphagia proposed by this group is a pathologic, insatiable hunger accompanied by abnormal food-seeking behaviors. Suggested methods to accurately identify patients with hyperphagia include increased physician and parent/caregiver education and standardized efficient screening procedures for use in the clinic. The etiology of hyperphagia as related to abnormal MC4R signaling was also reviewed and proposed as a central cause of the condition across several underlying diseases. Given this potential unified underlying pathology, the expert group recommends that patients with hyperphagia undergo genetic testing and that treatment include comprehensive weight-management strategies incorporating lifestyle and pharmacotherapies targeted at addressing hyperphagia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".