Exclusion of Older Adults from Ongoing Clinical Trials on Low Back Pain: A Review of the WHO Trial Registry Database
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The burden of low back pain (LBP) is high, especially for older adults who experience a higher number of years living with a disability. However, this population is not being well represented in clinical trials (CTs). This study analyzed the International Clinical Trial Registry Platform (ICTRP) database from the World Health Organization to verify the future trend in the participation of older adults in registered CTs on LBP. DESIGN: We performed a cross-sectional review of the ICTRP searching for prospective protocols planning interventions for LBP with registration dates from January 2015 through November 2018. From the protocols of the eligible studies, we extracted those planning to include older adults. RESULTS: A total of 167 protocols for CTs for LBP were planning to recruit participants older than 65 years. However, only five registries (2.99%; pooled sample = 169 participants) were designed to target participants specifically older than 65 years. The exclusion of older participants was not justified and imposed through an arbitrary upper-age limit in 93.6% of the protocols. Most of the protocols are from single-center studies, and a greater number are planned to be carried out in developed regions. Higher interest was in pharmacologic interventions, devices/technology, and physical rehabilitation. CONCLUSION: Older adults with LBP will continue to be underinvestigated in CTs for LBP in the near future. In general, ongoing trials are small, planned in developed regions, and proposing pharmacologic interventions to deal with LBP. J Am Geriatr Soc 67:603-608, 2019.
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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 | 0.179 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".