Prospective Registration and Outcome-Reporting Bias in Randomized Controlled Trials of Eczema Treatments: A Systematic Review
Bibliographic record
Abstract
We assessed completeness of trial registration and the extent of outcome-reporting bias in published randomized controlled trials (RCTs) of eczema (atopic dermatitis) treatments by surveying all relevant RCTs published from January 2007 to July 2011 located in a database called the Global Resource of Eczema Trials (GREAT). The GREAT database is compiled by searching six bibliographic databases, including EMBASE and MEDLINE. Out of 109 identified RCTs, only 37 (34%) had been registered on an approved trial register. Only 18 out of 109 trials (17%) had been registered "properly" in terms of submitting the registration before the trial end date and nominating a primary outcome. The proportion of "any registered" and "properly registered" RCTs increased from 19% and 10% in 2007 to 57% and 36% in 2011, respectively. Assessment of selective outcome-reporting bias was difficult even among the properly registered trials owing to unclear primary outcome description especially with regard to timing. Only 5 out of the 109 trials (5%) provided enough information for us to be confident that the outcomes reported in the published trial were consistent with the original registration. Adequate trial registration and description of primary outcomes for eczema RCTs is currently poor.
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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 | 0.164 | 0.376 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".