An Introduction to the Fundamentals of Randomized Controlled Trials in Pharmacy Research
Bibliographic record
Abstract
The randomized controlled trial (RCT) is regarded as one of the most valued research methodologies for examining the efficacy or effectiveness of interventions.1 Randomized trials are most often associated with studies of drug effectiveness; however, they have also been successfully applied to research questions related to provision of care by pharmacists.2-5 This article is not intended to be an exhaustive guide on performing an RCT, but rather an introduction to the major concepts and approaches involved in designing and conducting an RCT. Readers requiring additional information are referred to more comprehensive publications6-9 and are encouraged to consult with researchers experienced in this area before undertaking a study. Throughout this paper, individuals enrolled in a study are referred to as “patients”, the individuals delivering the study (e.g., pharmacists in practice research) are referred to as the “investigators”, and those responsible for study design and analysis are referred to as the “study team”. DESIGN OF A RANDOMIZED CONTROLLED TRIAL
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 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 | 0.174 | 0.270 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".