Physician Adherence to Local Treatment Policies and American Society of Hematology (ASH) Quality Metrics in Chronic Lymphocytic Leukemia (CLL) Management.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".