Clinical trials for symptoms in patients receiving dialysis
Bibliographic record
Abstract
Symptoms in patients receiving dialysis are common and associated with impaired quality of life. Symptoms are a top research priority because effective therapies are lacking and even with appropriate diagnosis and treatment, residual symptoms often persist. Clinical trials in the setting of kidney disease are challenging to conduct and as a result, nephrology lags behind other specialties regarding the degree to which clinical trials inform the care of patients with kidney disease, including those receiving dialysis. The studies in this thesis inform the design of randomized controlled trials with regards to run-in periods and the treatment of symptoms in patients with kidney disease. Chapter 2 describes a meta-epidemiologic study of the frequency, setting and purposes of run-in periods in parallel randomized controlled trials of self-administered medications for chronic diseases in adults. Chapter 3 is a study within a trial of an international randomized controlled trial that compares spironolactone to placebo for the prevention of cardiovascular morbidity and mortality in dialysis. It compares the ability of a 3-week study visit in addition to a 7-week study visit during an active-run-in period to identify and exclude participants with non-adherence. Chapter 4 is a protocol for a randomized placebo controlled crossover trial of low fixed dose pharmacologic therapy for restless legs syndrome in hemodialysis that includes a placebo run-in period for adherence and tolerability. Chapter 5 is a survey of Canadian nephrologists regarding the use of cannabinoids for symptom management in patients with kidney disease and support for their use in clinical trials.
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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 | 0.099 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".