Training competencies in adult metabolic medicine: A survey of working adult metabolic medicine physicians
Bibliographic record
Abstract
The rapid expansion of the number of adult patients with inherited metabolic diseases (IMDs) has created demand for physicians with expertise in the field of adult metabolic medicine (AMM). Unfortunately, existing accredited training programs in this field are rare, and training programs in pediatric metabolic medicine cannot fully meet the needs of AMM physicians as the types of patients and the problems they face are different in the adult setting. We surveyed a group of working practitioners in AMM for input on what medical expert competencies they feel should be included as part of training programs in AMM. Through a modified Delphi process, 66 physicians from six continents reached consensus on a comprehensive list of training competencies in AMM. This list includes competencies from the fields of adult internal medicine, neurology, medical genetics, and pediatric metabolic medicine but also includes competencies not found in any of those programs, leading to the conclusion that the training needs for specialists in AMM cannot be met from any of these existing programs. We propose that AMM be considered a subspecialty separate from pediatric metabolic medicine and that accredited training programs in AMM be created using these medical expert competencies as part of a broader program design.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".