<p>Analysis of Funding Source and Spin in the Reporting of Studies of Intravitreal Corticosteroid Therapy for Diabetic Macular Edema: A Systematic Review</p>
Bibliographic record
Abstract
PURPOSE: This systematic review examined the relationship between industry funding and the presence of spin in high-impact studies evaluating intravitreal corticosteroid therapy for diabetic macular edema. METHODS: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE and Embase were systematically searched from inception through July 16, 2018, for randomized controlled trials and meta-analyses investigating the treatment of patients with diabetic macular edema using intravitreal corticosteroid therapy. Only studies published in English journals with an impact factor greater than 2 as per the Clarivate Analytics 2017 Journal Citation Report were included. The authors independently assessed study quality, funding source and the presence of reporting bias using a standardized datasheet. RESULTS: Title and abstract screening were completed on 7158 unique hits and full-text review yielded 44 included studies. Overall, there was correspondence between the wording of abstract conclusions and study results in 41/44 (93%) articles. Correspondence between abstract conclusions and significance of main outcome was present in 14/14 (100%) industry-funded and 27/30 (90%) nonindustry-funded studies. The odds ratio of industry funding being associated with noncorrespondence was 0.27 (95% CI: 0.01-5.61, p=0.54). The most common reason for noncorrespondence was the failure to mention rates of steroid-related intraocular pressure elevation. CONCLUSION: The results of this systematic review indicate that biased abstract outcome reporting is rare in published randomized controlled trials and meta-analyses of intravitreal corticosteroid therapy for diabetic macular edema. Biased reporting was not associated with the presence of industry funding or a conflict of interest.
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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 | 0.147 | 0.438 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".