Clinician-Led Improvement in Cancer Care (CLICC): Complementing Evidence-Based Medicine with Evidence-Based Implementation
Bibliographic record
Abstract
This thesis explores whether a multifaceted intervention implemented through the NSW Agency for Clinical Innovation (ACI) Urology Clinical Network can improve the rates of referral of men with high-risk prostate cancer post-radical prostatectomy for consideration for adjuvant radiotherapy in line with clinical practice guideline recommended care. It comprises seven iterative studies that address urologists’ knowledge, attitudes and equipoise for the use of adjuvant radiotherapy for high-risk prostate cancer, the development of a clinical network embedded intervention and the evaluation of this intervention within a step-wedge cluster randomised trial ‘Clinician-Led Improvement in Cancer Care (CLICC)’ (NHMRC Partnership Grant 1011474; Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12611001251910). The thesis found some evidence that the CLICC intervention resulted in desired practice change. Results are presented within the context of the CLICC conceptual program logic framework and are interpreted in relation to knowledge, attitudes and beliefs in the wider urological community. The thesis concludes with consideration of how findings could be translated to the implementation of other clinical practice guideline recommendations.
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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".