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Enregistrement W1980263744 · doi:10.1159/000358105

A Roadmap for CAM Research towards the Horizon of 2020

2014· editorial· en· W1980263744 sur OpenAlexaboutno aff
Harald Walach, Sirpa Pietikäinen

Notice bibliographique

RevueComplementary Medicine Research · 2014
Typeeditorial
Langueen
DomaineMedicine
ThématiqueComplementary and Alternative Medicine Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChinaWork (physics)Political scienceResource (disambiguation)State (computer science)Public relationsEconomic growthComputer scienceEngineeringLaw

Résumé

récupéré en direct d'OpenAlex

CAMbrella was the first pan-European research project that systematically evaluated the state of usage, motivation, provision, and regulation of CAM usage in European countries. It also documented the need and the way forward for research in Europe. Some of the finest minds in European CAM research were either part of the consortium or were invited as experts to some of the specialist meetings. Thus, CAMbrella formulates a consensus never seen in European research on this topic before. Results of the work packages – most of them systematic reviews – have been published, also in open access format in FORSCHENDE KOMPLEMENTARMEDIZIN [1]. Now, the final piece, roadmap 2020, has been published and is available online [2]. This roadmap sums up the findings briefly and points towards the future direction of research. You do not have to be a wizard to understand the most important message: European research was once at the forefront of research in this topic, and it is in danger of becoming last, being overtaken by countries such as USA, Canada, Australia, India, China, Africa even. All these countries and continents have either formulated a research agenda (Canada), or have dedicated institutes that have funding available (USA, Australia, India), or have at least understood that traditional approaches to medicine are a resource (India, China, Africa). In USA, a steady funding stream of approximately USD 120 million per year enables the maintenance of a proper research agenda. What about Europe? Apart from isolated pockets full of projects: nothing. UK, often a forerunner, spends 0.0085% of its research budget on CAM research, where 10% of the population use CAM approaches each year and approximately 50% are lifetime users. The figures are even lower for Germany which is among the countries of the highest prevalence of usage [3]. European researchers were among those that were invited to the first foundational conferences of the Office of Alternative Medicine at the National Institutes of Health (NIH) in the 1990s, because of innovative research design and because CAM research has had a long tradition in Europe. What happened afterwards? The USA saw that this was a growth market, supported research by founding a National Center for CAM research (NCCAM), and became world leader in research in this area [4]. And Europe? A few projects to have a placating answer for the public in the drawer, but nothing serious. We feel we are at the brink of an important junction in history. If we want to continue improving the health of European citizens, we cannot ignore that CAM is a potentially important player, being used by up to 60% of the population [5]. It is important to realize that we know very little about its comparative effectiveness vis-a-vis conventional approaches. We do not know whether and when it would be beneficial for patients to integrate CAM treatments into their conventional treatment regime. We do not know how many patients would want that. We do not know which treatments would be safe. And we do not know how it happens that seemingly strange treatments can produce such strong effects that in a time of abundance of medical provision people are prepared to pay out of their pocket for such treatments. This in itself is a topic of highest interest. But CAM would have other things in stock that answer well to consensus goals and strategies of the European Union: Antibiotic resistance is a major threat to the future health of our population. Developing more antibiotics will not help (and will probably not happen for economic reasons). How about a different strategy? Various CAM approaches might offer alternatives here, but we do not know enough about them, because they are not being researched. The major challenges to the threat of health will be chronic and degenerative diseases. Those will be very difficult and costly to treat with approaches that are designed for acutetype interventions, and will rarely be healed by them for good.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,051
score de la tête « metaresearch » (Gemma)0,057
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,080
Score d'incertitude au seuil0,270

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0510,057
Méta-épidémiologie (sens strict)0,0040,002
Méta-épidémiologie (sens large)0,0050,006
Bibliométrie0,0070,005
Études des sciences et des technologies0,0040,004
Communication savante0,0130,027
Science ouverte0,0060,014
Intégrité de la recherche0,0220,015
Charge utile insuffisante (le modèle a refusé de juger)0,0800,028

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.

Tête enseignante Opus0,395
Tête enseignante GPT0,568
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2014
Routes d'admission1
Résumé présentoui

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