Initiatives to enhance referral patterns from primary care to specialist kidney care:a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives To highlight data on interventions aimed at enhancing referral patterns from primary care to nephrology. Design Systematic review and meta-analysis. Data sources MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science and PsycINFO. Eligibility criteria Studies reporting interventions aimed at decreasing referral volume, reducing wait times and/or increasing guideline-concordant referrals in adult patients with chronic kidney disease from primary care to specialist kidney care, from inception to 15 April 2024. Data extraction and synthesis Studies were subcategorised based on taxonomy of initiatives, type of intervention and single versus multi-faceted interventions. Our primary outcomes were changes to referral volume, wait times and proportion of guideline-concordant referrals. Data were pooled using a random effects model and a meta-analysis was conducted using pooled mean difference (MD) and OR. Results 20 studies used single and 13 used more than one intervention. Overall, interventions resulted in an increase in total referrals (MD: 19 referrals per month, 95% CI 7 to 30; I 2 =98%; p<0.001) and a non-significant decrease in the proportion of guideline-concordant referrals (OR: 0.32, 95% CI 0.09 to 1.16; I 2 =100%; p=0.08). These effects were consistent when stratified by single versus multi-faceted interventions, and by taxonomy of interventions. Interventions categorised as financial, regulatory or legislative incentives (n=2) led to an increase in guideline-concordant referrals (OR: 1.15, 95% CI 1.02 to 1.29; I 2 : 0%; p=0.02). Organisational changes showed a non-significant reduction in wait time of 24 days (95% CI −64 to 15, I 2 =99%; p=0.23) but tended to decrease guideline concordant referrals (OR: 0.17, 95% CI 0.03 to 0.86; I 2 : 100%; p=0.03). Conclusion Organisational changes decreased wait times. Overall, interventions increased referral volume and decreased guideline-concordant referrals. The data have implications for designing programmes to improve primary care-nephrology interface in kidney disease management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".