Development of quality indicators for treatment of ductal carcinoma in situ (DCIS) of the breast using a multidisciplinary Delphi process.
Bibliographic record
Abstract
248 Background: Ductal carcinoma in situ (DCIS) of the breast accounts for ~30% of new breast cancer diagnoses. Measuring quality of DCIS treatment is problematic due to its distinctively different clinical behaviour from invasive breast carcinoma, where standard outcomes such as mortality are not relevant. Therefore, we sought to develop clinically relevant quality indicators to evaluate treatment of DCIS. Methods: A Delphi consensus process was undertaken using a multidisciplinary panel of nine clinical and methodologic experts from Ontario, Alberta, and British Columbia. Panel members were nominated based on membership in provincial breast tumour site groups. Four criteria for a good quality indicator were used; the indicator measures a treatment that benefits the patient, there is support from scientific literature or professional consensus for benefit; the indicator is under control of the health care provider, the indicator is extractable from the medical record. Candidate indicators were identified from published clinical practice guidelines in North America. Three iterations of ratings using Likert scale rankings were utilized to identify final quality indicators, which were then prioritized. Results: A total of 10 candidate indicators were identified from four clinical practice guidelines encompassing the diagnosis, surgery and adjuvant treatment components of DCIS. A total of eight indicators were identified and prioritized (Table). Conclusions: We successfully developed practical quality indicators for evaluating the treatment of DCIS, which can be used in any jurisdiction to measure key performance benchmarks and identify variations in care warranting intervention or improvement. [Table: see text]
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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 | 0.197 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".