Clinical Practice Guidelines Adherence and the Impact on Health Outcomes in Oncology: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Aim Clinical practice guidelines (CPGs) are developed to standardize care by providing physicians and decision‐makers with evidence‐based recommendations. This study analyzed the effects of guideline adherence on clinical outcomes in patients with cancer. Methods Five electronic databases were searched through December 2024. Inclusion criteria were randomized control trials (RCT), prospective and retrospective cohort studies on patients with cancer managed with CPG adherent vs. non‐adherent care. Two reviewers screened studies for inclusion, extracted data and assessed the quality of studies. Outcomes of interest included overall survival (OS), disease‐free survival (DFS), and quality of life. A random‐effects model was used to pool the results. Certainty of evidence (CoE) was assessed using the GRADE approach. Results Seventy‐five studies were included. Results suggest that adherence to CPG was associated with improved OS in all disease sites (hazard ratio [HR] = 0.64 (95% confidence interval [CI]: 0.59 to 0.70); p < 0.0001, I2: 97%; low CoE). Subgroup analysis by disease site showed similar results except for gynecological cancers where adherence showed no statistically significant improvement in OS (HR = 0.76 (95% CI: 0.53 to 1.08); p = 0.13, I2: 77%; low CoE). Further analysis of studies in breast cancer with 5 years follow‐up average showed improvement in OS (HR = 0.70 (95% CI: 0.64 to 0.77); p < 0.001, I2: 0%; low CoE). Conclusion Evidence suggests that clinician's adherence to CPG recommendations may improve survival outcomes in patients with cancer. However, the evidence is largely based on retrospective observational studies, highlighting need for prospective trials to further elucidate these associations and better guide clinical practice.
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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 | 0.029 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".