Assessing protocol adherence in a clinical trial with ordered treatment regimens: Quantifying the pragmatic, randomized optimal platelet and plasma ratios (PROPPR) trial experience
Bibliographic record
Abstract
BACKGROUND: Medication dispensing errors are common in clinical trials, and have a significant impact on the quality and validity of a trial. Therefore, the definition, calculation and evaluation of such errors are important for supporting a trial's conclusions. A variety of medication dispensing errors can occur. In this paper, we focus on errors in trials where the intervention includes multiple therapies that must be given in a pre-specified order that varies across treatment arms and varies in duration. METHODS: The Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) trial was a Phase III multi-site, randomized trial to compare the effectiveness and safety of 1:1:1 transfusion ratios of plasma and platelets to red blood cells with a 1:1:2 ratio. In this trial, these three types of blood products were to be transfused in a pre-defined order that differed by treatment arm. In this paper, we present approaches from the PROPPR trial that we used to define and calculate the occurrence of out of order blood transfusion errors. We applied the proposed method to calculate protocol adherence to the specified order of transfusion in each treatment arm. RESULTS: Using our proposed method, protocol adherence was greater in the 1:1:1 group than in the 1:1:2 group (96% vs 93%) (p<0.0001), although out of order transfusion errors in both groups were low. Final transfusion ratios of plasma to platelets to red blood cells for the 1:1:1 ratio group was 0.93:1.32:1, while the transfusion ratio for the 1:1:2 ratio group was 0.48:0.48:1. CONCLUSIONS: Overall, PROPPR adherence to blood transfusion order pre-specified in the protocol was high, and the required order of transfusions for the 1:1:2 group was more difficult to achieve. The approaches proposed in this manuscript were useful in evaluating the PROPPR adherence and are potentially useful for other trials where a specific treatment orders with varying durations must be maintained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".