Development of Quality Metrics to Evaluate Pediatric Hematologic Oncology Care in the Outpatient Setting
Bibliographic record
Abstract
There are currently no clinic-level quality of care metrics for outpatient pediatric oncology. We sought to develop a list of quality of care metrics for a leukemia-lymphoma (LL) clinic using a consensus process that can be adapted to other clinic settings. Medline-Ovid was searched for quality indicators relevant to pediatric oncology. A provisional list of 27 metrics spanning 7 categories was generated and circulated to a Consensus Group (CG) of LL clinic medical and nursing staff. A Delphi process comprising 2 rounds of ranking generated consensus on a final list of metrics. Consensus was defined as ≥70% of CG members ranking a metric within 2 consecutive scores. In round 1, 19 of 27 (70%) metrics reached consensus. CG members' comments resulted in 4 new metrics and revision of 8 original metrics. All 31 metrics were included in round 2. Twenty-four of 31 (77%) metrics reached consensus after round 2. Thirteen were chosen for the final list based on highest scores and eliminating redundancy. These included: patient communication/education; pain management; delay in access to clinical psychology, documentation of chemotherapy, of diagnosis/extent of disease, of treatment plan and of follow-up scheme; referral to transplant; radiation exposure during follow-up; delay until chemotherapy; clinic cancellations; and school attendance. This study provides a model of quality metric development that other clinics may use for local use. The final metrics will be used for ongoing quality improvement in the LL clinic.
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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 | 0.129 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".