Notice bibliographique
Résumé
Because little was known concerning the scope of treatment of latent tuberculosis infection (LTBI) in the United States and Canada, identification of the types of clinics that administered such treatment, and patients who received it, would guide resource utilization and improve treatment initiation and completion. Sterling and coworkers, on behalf of the Tuberculosis Epidemiologic Studies Consortium, Centers for Disease Control and Prevention, surveyed 244 clinics, each having initiated LTBI treatment for 10 or more patients in 2002, at 19 U.S. and 2 Canadian sites (1). An estimated 37,857 patients started LTBI treatment in 2002, including 37,145 from the U.S. sites, with 79% at general public health clinics, 6.4%at immigrant/refugee clinics, and 6.1% at correction/detention facilities. Study catchment areas for the 19 U.S. sites represented 8.6% of the U.S. population and 12.7% of all tuberculosis (TB) cases in 2000. On extrapolation to the entire U.S. population, the estimated total number of LTBI treatment starts was approximately 291,000 to 433,000. Assuming a 5% lifetime risk of TB without treatment, and 20 to 60% treatment effectiveness, approximately 4,000 to 11,000 cases of TB were prevented in the United States. Thus, Sterling and coworkers concluded that treatment for LTBI was initiated among a substantial number of persons in theUnited States andCanada, primarily in the public sector, and such treatment could significantly decrease the disease burden in these countries. Targeted screening and treatment of latently infected subjects are central to strategies aimed at eliminating TB. Unfortunately, there appear to be few specific criteria, other than medical factors, in designating groups as high risk for developing TB. Moonan and coworkers conducted location-based screenings in partnership with multiple community-based organizations in communities previously demonstrated by geographic information system to have genotypically clustered Mycobacterium tuberculosis isolates (2). One person with TB was found for every 83 screened, and one person with LTBI for every five screened, far exceeding the expected yield of untargeted screening for a county with a TB incidence of only 5.7 per 100,000. Male subjects were more commonly identified (odds ratio [OR], 4.8). Thus, it appeared that combining genotyping and geographic information systems could potentially help in identifying high-risk status and in determining areas for location-based TB screening. In an editorial accompanying Moonan and colleagues’ article (2), it was pointed out that these data could be used as a tool for garnering the critical support of community-based organizations (3). Both shortand long-term benefits of such partnerships as well as the resulting interventions are important to measure. In addition to following up on the outcome of the intervention, an analysis of the services received by the screened individuals would clarify the relative roles of housing, correctional care
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,082 | 0,060 |
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 ».