Does a quality-of-care problem exist? Cancer Care Ontario practice pattern data and the recommendations of two lung cancer practice guidelines.
Bibliographic record
Abstract
190 Background: Practice-pattern data, evidence-based knowledge and transfer, and performance management strategies define Cancer Care Ontario’s quality improvement strategy. Knowledge products, such as practice guidelines, are intended to provide recommendations for practice, based on best available evidence, to improve quality of care and reduce variation in practice. Review of 2010-2011 Cancer System Quality Index (CSQI) data revealed complex practice patterns in treatment of non-small cell lung cancer patients with stages II and IIIa resected and stages IIIa and IIIb non-resected disease in Ontario. A multi-method study was initiated to understand the patterns, to identify if a quality of care problem exists and to propose improvements moving forward. Methods: Surgeons, medical oncologists and radiation oncologists from Ontario were invited to participate in a survey consisting of 6 areas of inquiry. A grounded theory approach was used to guide key informant interviews of purposively sampled clinicians and administrators. A more in-depth analysis of the CSQI data was planned. Results: Clinicians responding to survey provided positive assessments of PG recommendations and evidentiary base; perceptions of practice patterns were less problematic than hypothesized; estimates of benchmarks were highly variable; and assessments of barriers to recommendation implementation included slow referral process, lack of organization support and patients seen in practice not reflected in the evidence. From the interviews, 5 themes emerged: unique patient, unique physician, family, clinical team, and clinical evidence. Further analysis of CSQI data was not possible given limitations related to data collection. Conclusions: A perceived quality of care problem initiated this study. Concerns centred on significant proportion of patients receiving no treatment; modest percentage of patients receiving treatment that aligned with PG recommendations; and regional variation within each of the clinical care options. Our data show that defining a quality of care problem is significantly more complex than consideration of practice patterns alone.
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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 | 0.029 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".