Randomized controlled trials – The what, when, how and why
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are at the top of the pyramid of evidence as they offer the best answer on the efficacy of a new treatment. RCTs are true experiments in which participants are randomly allocated to receive a certain intervention (experimental group) or a different intervention (comparison group), or no treatment at all (control or placebo group). Randomization, along with other methodological features such as blinding and allocation concealment, safeguard against biases. This review will focus on parallel group RCT design as it is the most common design in the field of Pediatric Urology. RCTs can be designed using a superiority, equivalency, or non-inferiority hypothesis, and are usually preceded by a pilot, where the trial protocol is implemented in a small number of patients, mimicking the larger, definitive study. Even though regarded as the best available option to bring out scientific data, RCTs might be prone to mislead. If RCTs are small and underpowered, a difference of even one single event between groups, may completely change the trial results. To safeguard against RCTs weakness, a fragility concept of statistical significance was developed and called the Fragility Index (FI). RCTs may not be appropriate, ethical, or feasible for all surgical interventions. They may have limitations such as prohibitive cost and unrealistic large sample sizes. Nearly 60 % of surgical research questions cannot be answered by RCTs. Therefore, clinical practice should be based on the best available evidence on a given topic, regardless of the study design. However, even in these situations, conclusions drawn from observational studies must be interpreted with caution.
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 | 0.241 | 0.475 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".