Primary outcome reporting in clinical trials for older adults with depression
Bibliographic record
Abstract
BACKGROUND: Findings from randomised controlled trials (RCTs) are synthesised through meta-analyses, which inform evidence-based decision-making. When key details regarding trial outcomes are not fully reported, knowledge synthesis and uptake of findings into clinical practice are impeded. AIMS: Our study assessed reporting of primary outcomes in RCTs for older adults with major depressive disorder (MDD). METHOD: Trials published between 2011 and 2021, which assessed any intervention for adults aged ≥65 years with a MDD diagnosis, and that specified a single primary outcome were considered for inclusion in our study. Outcome reporting assessment was conducted independently and in duplicate with a 58-item checklist, used in developing the CONSORT-Outcomes statement, and information in each RCT was scored as 'fully reported', 'partially reported' or 'not reported', as applicable. RESULTS: Thirty-one of 49 RCTs reported one primary outcome and were included in our study. Most trials (71%) did not fully report over half of the 58 checklist items. Items pertaining to outcome analyses and interpretation were fully reported by 65% or more of trials. Items reported less frequently included: outcome measurement instrument properties (varied from 3 to 30%) and justification of the criteria used to define clinically meaningful change (23%). CONCLUSIONS: There is variability in how geriatric depression RCTs report primary outcomes, with omission of details regarding measurement, selection, justification and definition of clinically meaningful change. Outcome reporting deficiencies may hinder replicability and synthesis efforts that inform clinical guidelines and decision-making. The CONSORT-Outcomes guideline should be used when reporting geriatric depression RCTs.
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 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 | 0.649 | 0.864 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".