Factors associated with reporting of the Prevention of Falls Network Europe (ProFaNE) core outcome set domains in randomized trials on falls in older people: a citation analysis and correlational study
Bibliographic record
Abstract
Abstract Background Core outcome sets are advocated as a means to standardize outcome reporting across randomized controlled trials (RCTs) and reduce selective outcome reporting. In 2005, the Prevention of Falls Network Europe (ProFaNE) published a core outcome set identifying five domains that should be measured and reported, at a minimum, in RCTs or meta-analysis on falls in older people. As reporting of all five domains of the ProFaNE core outcome set has been minimal, we set out to investigate factors associated with reporting of the ProFaNE core outcome set domains in a purposeful sample of RCTs on falls in older people. Methods We conducted a systematic citation analysis to identify all reports of RCTs focused on falls in older people that cited the ProFaNE core outcome set between October 2005 and July 2021. We abstracted author-level, study-level, and manuscript-level data and whether each domain of the ProFaNE core outcome set was reported. We used penalized LASSO regression to identify factors associated with the mean percentage of ProFaNE core outcome set domains reported. Results We identified 85 eligible reports of RCTs. Articles were published between 2007 and 2021, described 75 unique RCTs, and were authored by 76 unique corresponding authors. The percentage of ProFaNE core outcome set domains reported ranged from 0 to 100%, with a median of 40% and mean (standard deviation, SD) of 52.2% (25.1). RCTs funded by a non-industry source reported a higher mean percentage of domains than RCTs without a non-industry funding source (estimated mean difference = 17.5%; 95% confidence interval (CI) 1.8–33.2). RCTs examining exercise (15.4%; 95% CI 1.9–28.9) or multi-component/factorial (17.4%; 95% CI 4.7–30.1) interventions each reported a higher mean percentage of domains than RCTs examining other intervention types. Conclusions We found that RCTs funded by at least one non-industry source, examining exercise or multi-component/factorial interventions, reported the highest percentages of ProFaNE core outcome set domains. Findings may help inform strategies to increase the impact of the ProFaNE core outcome set. Ultimately, this may lead to enhanced knowledge of the effectiveness and safety of interventions to prevent and/or manage falls in older people.
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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 | 0.424 | 0.848 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.023 | 0.032 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| 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".