Designing and validating a clinical decision support algorithm for diabetic nephroprotection in older patients
Bibliographic record
Abstract
BACKGROUND: Older patients with diabetic kidney disease (DKD) often do not receive optimal pharmacological treatment. Current clinical practice guidelines (CPGs) do not incorporate the concept of personalised care. Clinical decision support (CDS) algorithms that consider both evidence and personalised care to improve patient outcomes can improve the care of older adults. The aim of this research is to design and validate a CDS algorithm for prescribing renin-angiotensin-aldosterone system inhibitors (RAASi) for older patients with diabetes. METHODS: The design of the CDS tool included the following phases: (1) gathering evidence from systematic reviews and meta-analyses of randomised clinical trials to determine the number needed to treat (NNT) and time-to-benefit (TTB) values applicable to our target population for use in the algorithm. (2) Building a list of potential cases that addressed different prescribing scenarios (starting, adding or switching to RAASi). (3) Reviewing relevant guidelines and extracting all recommendations related to prescribing RAASi for DKD. (4) Matching NNT and TTB with specific clinical cases. (5) Validating the CDS algorithm using Delphi technique. RESULTS: We created a CDS algorithm that covered 15 possible scenarios and we generated 36 personalised and nine general recommendations based on the calculated and matched NNT and TTB values and considering the patient's life expectancy and functional capacity. The algorithm was validated by experts in three rounds of Delphi study. CONCLUSION: We designed an evidence-informed CDS algorithm that integrates considerations often overlooked in CPGs. The next steps include testing the CDS algorithm in a clinical trial.
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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 | 0.094 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".