The right tool for the job—Fit for purpose training programs in adult metabolic medicine
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,008 |
| Science ouverte | 0,003 | 0,015 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,032 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».