NAVKIDS2 trial: a multi-centre, waitlisted randomised controlled trial of a patient navigator intervention in children with chronic kidney disease — statistical analysis plan and update to the protocol
Bibliographic record
Abstract
BACKGROUND: trial of patient navigators for children with chronic kidney disease (CKD) experiencing social disadvantage and provides the statistical analysis plan (SAP) which has not previously been published. METHODS/DESIGN: The original protocol was published in BMC Nephrology ( https://doi.org/10.1186/s12882-019-1325-y ) prior to the commencement of trial recruitment. During the course of the trial, some key methodological changes needed to be made including changes to eligibility criteria (addition of patients with CKD stages 1-2, broadening of financial status eligibility criterion, addition of patients living in rural/remote areas, modification of age eligibility to 0-16 years, addition of limits related to the language spoken by family, guidance regarding families with multiple eligible children), changes to sites, reduction of sample size, addition of virtual options for consent and study procedures in response to the COVID-19 pandemic, removal of staggered recruitment across sites, addition of outcomes, and changes to the timing and number of assessments. This update summarises the changes made and their rationale and provides the detailed plan for statistical analysis of the trial. These changes have been finalised prior to the completion of study follow-up and the commencement of data analysis. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12618001152213 . Prospectively registered on 12 July 2018.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".