Physician Adherence to Local Treatment Policies and American Society of Hematology (ASH) Quality Metrics in Chronic Lymphocytic Leukemia (CLL) Management.
Notice bibliographique
Résumé
Abstract Introduction: The assessment of adherence to health care quality indicators can provide a measure of the gap that exists between ideal evidence-based practice and actual care received by patients. Adherence to practice policies in chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) has not previously been documented. Methods: To determine physician adherence to performance measures and local treatment policies we completed a retrospective review of consecutive patients diagnosed with CLL or SLL and managed at a large regional multidisciplinary cancer center between Jan 2000 and Jan 2005. Patients were identified from the center administrative database according to ICD-0 histology codes. We identified quality metrics (process measures) from a literature review of practice guidelines and from the recently-devised ASH quality measures. Data were analysed using the Statistical Package for the Social Sciences (SPSS version 11.0, SPSS Inc., Chicago, IL). Results: A total of 149 patients were diagnosed with CLL/SLL and assessed at the centre. Thirty-seven were excluded because they were not diagnosed on site and were referred more than 6 months from the time of their original diagnosis; therefore, 112 patients remained and were evaluated further. The majority of patients were diagnosed with CLL (92%) with few patients identified as CLL/SLL (4%), SLL exclusively (2%), or diagnosis not documented (2%). Half of the group (52%) presented with Rai clinical stage 0 disease, 22% were Rai stage I/II and 11% Rai stage III/IV. Flow cytometry studies were completed according to the ASH quality metrics for CLL in 89% of all patients. Seventy-two percent of patients underwent imaging with CT or ultrasound for the purposes of staging. After a median follow-up time of 2.2 years, 73% of the patients were still following a watch and wait (observation) management strategy without having received therapy. Overall survival at 3 years was 97%. Of those that had undergone their first treatment, the most common therapy was chlorambucil (67%), followed by fludarabine (13%), combination akylator-based chemotherapy (7%), and clinical trial options (3%). The majority of patients (68%) were counseled for smoking avoidance, while only 22% were counseled to obtain vaccinations. Few physicians (9%) routinely counseled their patients about the necessity for screening for second cancers. Physicians who saw a higher volume of CLL cases in the centre (>10% of cohort) were compared to lower volume physicians with respect to policy adherence. High-volume physicians were more likely than low-volume physicians to counsel patients regarding the potential role for stem-cell transplantation in CLL (18% vs. 5%; p=0.033) and the importance of smoking cessation (74% vs. 43%; p=0.0065). Low-volume physicians were more likely to counsel patients regarding screening for secondary cancers (24% vs. 5%; p=0.007). There was no significant association between volume of practice and the performance of flow cytometry in diagnosis (91% vs. 81%; p=0.17). Conclusions: Physician adherence to guidelines is highest in process measures associated with diagnosis and staging, but is suboptimal with respect to patient counseling on lifestyle and preventive health measures.
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 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».