Quality indicator selection for the Canadian Partnership against Cancer rectal cancer project: A modified Delphi study
Bibliographic record
Abstract
AIM: It is well established that (i) magnetic resonance imaging, (ii) multidisciplinary cancer conference (MCCs), (iii) preoperative radiotherapy, (iv) total mesorectal excision surgery and (v) pathological assessment as described by Quirke are key processes necessary for high quality, rectal cancer care. The objective was to select a set of multidisciplinary quality indicators to measure the uptake of these clinical processes in clinical practice. METHOD: A multidisciplinary panel was convened and a modified two-phase Delphi method was used to select a set of quality indicators. Phase 1 included a literature review with written feedback from the panel. Phase 2 included an in-person workshop with anonymous voting. The selection criteria for the indicators were strength of evidence, ease of capture and usability. Indicators for which ≥90% of the panel members voted 'to keep' were selected as the final set of indicators. RESULTS: During phase 1, 68 potential indicators were generated from the literature and an additional four indicators were recommended by the panel. During phase 2, these 72 indicators were discussed; 48 indicators met the 90% inclusion threshold and included eight pathology, five radiology, 11 surgical, six radiation oncology and 18 MCC indicators. CONCLUSION: A modified Delphi method was used to select 48 multidisciplinary quality indicators to specifically measure the uptake of key processes necessary for high quality care of patients with rectal cancer. These quality indicators will be used in future work to identify and address gaps in care in the uptake of these clinical processes.
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 | 0.195 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".