Using quality indicators to monitor changes in adherence to clinical practice guidelines for treatment of ductal carcinoma in situ (DCIS) of the breast.
Bibliographic record
Abstract
246 Background: Evaluation of the management of DCIS poses challenges, as standard breast cancer outcome measures such as mortality do not apply. Therefore, quality indicators (QIs) were previously developed through an interprovincial, multidisciplinary Delphi process to assess the quality of DCIS treatment and adherence to clinical practice guidelines. Population based treatment of DCIS was then compared over time using these QIs. Methods: Patients diagnosed with DCIS from 2000 to 2001 and 2009 to 2011 were identified on a population basis from the Alberta Cancer Registry and a province-wide prospectively collected synoptic medical report (WebSMR) in Alberta, Canada. Patient charts were reviewed retrospectively and QIs abstracted. Results: A total of 620 patients were identified in the study periods. Results are summarized in the table. In comparison to the earlier group, the recent cohort showed significant improvement in radiation oncology referral, radiation post lumpectomy, and complete pathology reporting. Axillary staging significantly increased from 20% (axillary dissection in 2000 to 2001) to 73% (sentinel node biopsy 2009 to 2011). Other QIs did not differ significantly. Conclusions: Adherence to clinical practice guidelines for DCIS in Alberta measured using developed QIs have shown significant improvements, particularly in pathology reporting, referral to radiation oncologists and uptake of radiation therapy for patients treated with breast conserving surgery. Rates of sentinel node biopsy seem unacceptably high, which requires further investigation. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".