The right tool for the job—Fit for purpose training programs in adult metabolic medicine
Bibliographic record
Abstract
There has been a rapid growth in the population of both pediatric and adult patients with inherited metabolic diseases (IMDs) related to improvements in diagnostic technologies and therapeutic advancements.1, 2 If we are to be able to care for this burgeoning patient population, then we need the means to expand the metabolic workforce at the same pace. Unfortunately, the expansion of existing and novel diagnostic and therapeutic modalities for IMDs far outpaces changes in the content of medical education for IMDs and in the availability of training opportunities. There is a significant need for dedicated training programs to meet the demand for trained clinicians to care for adult patients with IMDs who comprise an increasing proportion of patients followed in IMD clinics. Although there is an inadequate number of training opportunities for pediatric metabolic medicine (PMM), this is even worse for adult care providers with only a single country (United Kingdom) having accredited training in adult metabolic medicine (AMM).3 Lack of access to qualified AMM specialists has been cited as one of the major barriers to care for the pediatric IMD population.2 The types of patients and problems faced by AMM physicians differ from those faced by pediatric metabolic specialists.4 Pediatric focused training programs (already underresourced and over-stretched) were not thought to adequately prepare physicians to practice AMM medicine5 so expanding these programs, while urgently needed to meet the needs of pediatric patients, will not solve the problem for adults. For these reasons, in addition to expanding the number of training opportunities for PMM, we need to both expand and change the opportunities available to train AMM specialists by developing training programs that are fit for purpose. In JIMD Reports,6 we present the results of a 2-year consultation process with working AMM physicians from around the world. We received input from 66 working AMM physicians across 6 continents. These physicians were highly experienced clinicians (60% had been in practice for 10 or more years and 48% followed 500 or more patients). The age of the respondents highlights the urgency to expand training opportunities for AMM specialists as more than 40% of them were over the age of 50 years and the development and accreditation of training programs is a slow and laborious task. Using the collective experience of this group, a consensus statement on a list of medical expert training competencies for practitioners in AMM was developed using a modified Delphi process. The list of competencies is divided into 10 areas, including management of transition, pregnancy, long-term complications, and skills of critical appraisal as they apply to rare diseases. A sample program structure for subspecialty training in AMM to accommodate trainees from different specialty backgrounds was added to give an example on the practical use of the document. Defining what AMM specialists actually need to know can help kickstart work around the world to develop training for AMM specialists to meet the needs of the rapidly expanding patient population.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.032 |
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".