Clinical decision support to improve blood pressure control in hemodialysis patients: a nonrandomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Computer-based clinical decision support aims to improve the quality of patient care. The utility of decision support for improving blood pressure control in hemodialysis patients is unknown. METHODS: This was a nonrandomized controlled trial of adult patients receiving chronic in-center hemodialysis during the period of April 1, 2005, to September 30, 2006, in 1 of the 2 major university-based renal programs in Alberta, Canada. Physicians in the intervention center were provided with twice-monthly audits and printed management suggestions based on guideline-recommended blood pressure targets. The same data were available to physicians in the control group but without audit and feedback decision support. RESULTS: Eight hundred and thirty hemodialysis patients were receiving dialysis treatment at the time the study was initiated. Preintervention and postintervention blood pressure data were available for 361 patients. The primary outcome, the proportion of postdialysis systolic blood pressures at target over 12 months, did not differ between the intervention and the control programs (unadjusted odds ratio 0.59; 95% confidence interval [95% CI], 0.34-1.02, p = 0.06; adjusted odds ratio 0.62; 95% CI, 0.35-1.11, p = 0.11). There was no significant difference between the intervention and control groups in other measures of blood pressure such as the mean change in postdialysis systolic blood pressures (unadjusted mean difference 4 mm Hg, 95% CI, -1 to 9, p = 0.36; adjusted mean difference 2 mm Hg, 95% CI, -1 to 5, p = 0.19). CONCLUSIONS: In this population of chronic hemodialysis patients, a computer-based clinical decision support system was not associated with improved blood pressure control.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".