Identification of Performance Indicators across a Network of Clinical Cancer Programs
Bibliographic record
Abstract
BACKGROUND: Cancer quality indicators have previously been described for a single tumour site or a single treatment modality, or according to distinct data sources. Our objective was to identify cancer quality indicators across all treatment modalities specific to breast, prostate, colorectal, and lung cancer. METHODS: Candidate indicators for each tumour site were extracted from the relevant literature and rated in a modified Delphi approach by multidisciplinary groups of expert clinicians from 3 clinical cancer programs. All rating rounds were conducted by e-mail, except for one that was conducted as a face-to-face expert panel meeting, thus modifying the original Delphi technique. Four high-level indicators were chosen for immediate data collection. A list of confounding variables was also constructed in a separate literature review. RESULTS: A total of 156 candidate indicators were identified for breast cancer, 68 for colorectal cancer, 40 for lung cancer, and 43 for prostate cancer. Iterative rounds of ratings led to a final list of 20 evidence- and consensus-based indicators each for colorectal and lung cancer, and 19 each for breast and prostate cancer. Approximately 30 clinicians participated in the selection of the breast, lung, and prostate indicators; approximately 50 clinicians participated in the selection of the colorectal indicators. CONCLUSIONS: The modified Delphi approach that incorporates an in-person meeting of expert clinicians is an effective and efficient method for performance indicator selection and offers the added benefit of optimal clinician engagement. The finalized indicator lists for each tumour site, together with salient confounding variables, can be directly adopted (or adapted) for deployment within a performance improvement program.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".