An Audit of Behcet’s Syndrome Research: A 10-year Survey
Bibliographic record
Abstract
OBJECTIVE: data suggest that the use of disease control groups and proper use of power calculations were neglected in published reports. We surveyed these and other methodological shortcomings in reports published within the last decade about one specific topic, Behcet's syndrome. We reason that recognizing such methodological shortcomings will lead to better quality clinical and basic science articles. METHODS: articles published in the 15 highest impact factor journals on rheumatology, ophthalmology, dermatology, and general medicine between January 1999 and January 2009 were searched for original reports on Behcet's syndrome. Study designs (study types and time element), control groups, demographic data, use of power calculations, and reporting of negative results were specifically tabulated. RESULTS: most studies on Behcet's syndrome were cross-sectional (83%). Prospective longitudinal studies were few (7%). In a considerable proportion of papers (21%), some basic demographic data were missing. Power calculations were rare (3%) even in randomized controlled trials and were not considered at all in clinical hypothesis-testing. Disease control groups were present in slightly over half of clinical and laboratory original research, while just 13% of genetic association studies included disease controls. Only 12% of all reports concerned mainly negative outcomes. CONCLUSION: a considerable number of the published research articles have methodological weaknesses. The generalizability of what we observed in Behcet's syndrome to other research topics needs to be formally studied.
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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 | 0.120 | 0.274 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".