Defining standard of practice: pros and cons of the usual care arm
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this review is to describe the use of usual care arms in randomized trials. RECENT FINDINGS: Randomization of patients to an experimental or a control arm remains paramount for the estimation of average causal effects. Selection of the control arm is as important as the definition of the intervention, and it might include a placebo control, specific standards of care, protocolized usual care, or unrestricted clinical practice. Usual care control arms may enhance generalizability, clinician acceptability of the protocol, patient recruitment, and ensure community equipoise, while at the same time introducing significant variability in the care delivered in the control group. This effect may reduce the difference in treatments delivered between the two groups and lead to a negative result or the requirement for a larger sample size. Moreover, usual care control groups can be subject to changes in clinician behavior induced by the trial itself, or by secular trends in time. SUMMARY: Usual care control arms may enhance generalizability while introducing significant limitations. Potential solutions include the use of pretrial surveys to evaluate the extent to which a protocolized control arm reflects the current standard of care and the implementation of adaptive trials.
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 | 0.094 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".