Notice bibliographique
Résumé
Patients with lipodystrophy run a risk of early coronary disease that is several times greater than the risk in the general population, physicians attending the recent Canadian Cardiovascular Society Congress were told. Dr. Robert Hegele, from the Robarts Institute at the University of Western Ontario, observed that in familial partial lipodystrophy there is absence of fat in the buttocks and arms, with central obesity. He noted a common ancestry among afflicted Canadians and discovered that the causative gene on chromosome 1q21 encoded nuclear lamin. The results indicate that defects in the nuclear envelope can have metabolic and, ultimately, cardiovascular consequences. Hegele has also been studying why there is an extraordinary 40% incidence of type 2 diabetes among some First Nations adults in Northern Ontario, versus the overall Canadian incidence of 8%. It appears that this is caused by an abnormality at a single residue in the amino acid sequence of hepatic nuclear factor-1 a, which appears unique to this population. Hegele then linked the emergence of diabetes to radical alterations in diet and lifestyle. Although this genetic trait was unimportant 50 years ago, it has now emerged as a key determinant of diabetes risk and a likely explanation of the increase in diabetes and coronary disease within the Aboriginal people of Northern Ontario. Dr. Simon Pimstone, from the Department of Medical Genetics at the University of British Columbia, addressed the role of pharmacogenomics in the treatment of cardiovascular disease. He suggested that the extensive genetic variability seen among humans has influenced interindividual variation in response to drugs. He said this variation has modulated, at least in part, the fatal adverse drug reactions that in 1999 accounted for more than 100 000 deaths in the US and were estimated to be the fifth leading cause of in-hosital death. Perhaps even more important is the strong likelihood that genetic factors will modulate the susceptibility to the beneficial effects of medications. For instance, for a particular pharmacogenetic marker, 49% of the population were low responders, 35% high responders and 16% nonresponders. At present, the only way for a clinician to tell which category a patient falls into would be empirical: try the medication, wait for the response and then make adjustments to dose or try another medication. However, if a patient's response — high, low or nonresponsive — could be predicted by a simple genetic screen, there would be less need for such empirical treatment and monitoring. In this way, the most appropriate treatment could be determined much sooner. Thus, the mass-marketing approach for drugs indicated for common conditions, such as hypertension, is unlikely to be the way of the future. Instead, genetic typing has the potential to “microsegment” the market in a boutique fashion. As a result, this may rescue some failed drugs and lead to changes in indications or even expand indications and appropriate patient populations for others. — This article was written by Dr. Paul Armstrong, an Edmonton cardiologist. Physicians interested in submitting similar reports should contact John Hoey, 800 663-7336 x2118; ac.amc@jyeoh.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,248 | 0,148 |
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 ».