MétaCan
Menu
Retour à la cohorte
Enregistrement W2045638037 · doi:10.1001/jama.290.1.115

Lessons and Responses in Alzheimer Disease Research

2003· article· en· W2045638037 sur OpenAlexaboutno aff
Peter J. Whitehouse

Notice bibliographique

RevueJAMA · 2003
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineClinical trialDiseasePsychological interventionDementiaClinical researchDrug developmentMultidisciplinary approachFamily medicinePsychiatryGerontologyPathologyLawDrug

Résumé

récupéré en direct d'OpenAlex

ALZHEIMER DISEASE (AD), THE MOST COMMON FORM OF DEmentia, affects about four million Americans and has been estimated to cost US society $100 billion per year, exceeded only by the costs of heart disease and cancer. The prevalence of AD has been predicted to reach 14 million by 2050 unless a treatment is found, and overall costs may increase four-fold. While considerable debate remains about the pathogenesis, nosology, and treatment of AD, there is no doubt that this research has the potential for enormous financial and professional gains. Thus, there is a need to balance the interests of both researchers and society in conducting AD research. Although the financial interests of clinical investigators have not necessarily affected the validity of trial results, experiences with some AD drug trials have prompted the development of organizational guidelines to limit the appearance or reality of financial conflicts of interest. Based on a trial of tacrine and other drugs, a multidisciplinary panel was convened to study the financial relationship between academia and industry. Its recommendations included proactive disclosure of both personal and organizational conflicts in all clinical trials, particularly as a way to build public trust in the drug development process. The Parkinson Study Group, a not-for-profit physicians’ group that coordinates research at 85 sites across the United States and Canada, has adopted similar principles and has published the results of some 25 multicenter trials for diagnostic methods and experimental interventions in Parkinson disease. This group further mandates review of all research by outside health care providers and the release of both positive and negative results to the public. Guided in part by the approach of the Parkinson Study Group, a large multisite study—the National Institute on Aging (NIA) Cooperative Study—has developed and internally disseminated conflict-of-interest guidelines. These include a $10000 annual cap on consulting fees for investigators and stringent limitations on ownership of equity in companies involved in the studies. Those leading the studies are subject to stricter guidelines. However, the blanket exclusion of experts with some industry ties from the design of trials might make drug development less efficient. Therefore, the Cooperative Study’s guidelines reflect a need to balance access to scientific expertise with the goal of mitigating conflicts of interest. The group is currently studying the impact of its guidelines on the conduct of clinical trials. Novel targets of AD pathogenesis, however, would present a new set of challenges even if all appearances of conflict were to be addressed. A recent trial of a vaccine-based treatment for AD was viewed by many as a critical test of the amyloid hypothesis, a popular model of AD pathogenesis. Vaccination of transgenic mice against components of human amyloid, a protein at the core of senile plaques in AD, led to clearance of this protein from the brain. However, in phase II human trials with this same vaccine, some subjects developed autoimmune encephalitis, an adverse effect that prompted termination of the trial. As others have suggested, the public health can best be served in this case by a full disclosure of the disease course and clinical response of all trial participants, rather than analyses of single cases or subsets of subjects. Equipped with as complete a set of positive and negative findings as possible, clinical investigators would be better able to anticipate potential problems with mechanistically novel agents in future trials.

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)0,160
score de la tête « metaresearch » (Gemma)0,218
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: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,160
Score d'incertitude au seuil0,846

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1600,218
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0050,003
Études des sciences et des technologies0,0090,027
Communication savante0,0220,038
Science ouverte0,0060,016
Intégrité de la recherche0,0360,053
Charge utile insuffisante (le modèle a refusé de juger)0,0230,007

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 Opus0,664
Tête enseignante GPT0,534
Écart entre enseignants0,130 · 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
GenreEmpirique

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

En bref

Citations2
Publié2003
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJAMAMême sujetHealth Systems, Economic Evaluations, Quality of LifeTravaux en français237 207