Validation of the global resource of eczema trials (GREAT database)
Bibliographic record
Abstract
BACKGROUND: Eczema (syn. Atopic Eczema or Atopic Dermatitis) is a chronic, relapsing, itchy skin condition which probably results from a combination of genetic and environmental factors. The Global Resource of EczemA Trials (GREAT) is a collection of records of randomised controlled trials (RCTs) for eczema treatment produced from a highly sensitive search of six reference databases. We sought to assess the sensitivity of the GREAT database as a tool to save future researchers repeating extensive bibliographic searches. METHODS: All Cochrane systematic review on treatments for eczema and five non-Cochrane systematic reviews on eczema were identified as a reference set to assess the utility of the GREAT database in identifying randomised controlled trials (RCTs). RCTs included in the systematic reviews were checked for inclusion in the GREAT database by two independent authors. A third author resolved any disagreements. RESULTS: Five Cochrane and six non-Cochrane systematic reviews containing a total of 105 RCTs of eczema treatments were included. Of these, 95 fitted the inclusion criteria for the GREAT database and 88 were published from 2000 onwards. Of the 88 eligible studies, 92% were found in the GREAT database. Seven trials were not included in the GREAT database - two of these were reported within a review paper and one as an abstract with no trial results. CONCLUSIONS: The sensitivity of the GREAT database for trials from 2000 onwards was high (75/88 trials, 94%). Sensitivity for the period prior to 2000 was less sensitive, due to differences in how the trials were identified prior to this time. 'Dual' filtering for new records has recently become part of the GREAT database methodology and should further improve the sensitivity of the database in time. The GREAT database can be considered as a primary source for future systematic reviews including randomised controlled trials of eczema treatments, but searches should be supplemented by checking reference lists for eligible trials, searching trial registries and contacting pharmaceutical companies for unpublished studies.
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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 | 0.398 | 0.758 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.046 | 0.039 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".