<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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".