Quality assessment of meta-analyses of RCTs of pharmacotherapy in major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Meta-analyses (MAs) of randomized controlled trials (RCTs) have the potential to provide the highest level of evidence, but the quality of published MAs has not been systematically assessed. Therefore, we determined reliability was significant (kappa = 0.89; p < 0.05). the quality of reporting in MAs of RCTs of pharmacotherapy for major depressive disorder (MDD) in adults (18-65 years) without comorbidities and examine trends over time. METHODS: MEDLINE, EMBASE, Healthstar, Psychlit and Cochrane databases were searched (1980-2002) by 4 independent reviewers for MAs of RCTs. Articles meeting inclusion criteria were blinded. Inter-rater reliability (kappa) was evaluated using a test-retest strategy on 4 articles. Quality was (p = 0.74) did not detect a difference in quality of assessed using the QUOROM checklist. Time trends were evaluated by calculating Spearman's rho. RESULTS: One hundred articles were identified, 68 were excluded [co-morbidities (9), inappropriate comparator (13), inappropriate outcome (15), article not available (5), inappropriate patient population (15), and inappropriate study design (11)]; 32 were included. Initial kappa was 0.81 (p < 0.05). After resolution of disagreements, the test-retest The mean overall quality score was 50.2% (SD 15.8%, range = 16.7-88.9%). The overall score for Titles was very poor (22%), Abstracts (40%) and Methods (49%) were poor, while overall Results score was minimally acceptable (54%). Good quality scores were found for Introduction (91%) and Discussion (97%). No time trends were identified using Spearman's correlation analysis (rho 0.05; p = 0.79). The Mann-Whitney U test articles published before and after the QUOROM. CONCLUSION: Despite quality guidelines, the average quality of published MAs of antidepressants is barely acceptable (50.2%). A need exists for adherence to standardized reporting and quality guidelines.
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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 | 0.409 | 0.655 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".