How have research questions and methods used in clinical trials published in <i>Clinical Rehabilitation</i> changed over the last 30 years?
Bibliographic record
Abstract
Research in rehabilitation has grown from a rare phenomenon to a mature science and clinical trials are now common. The purpose of this study is to estimate the extent to which questions posed and methods applied in clinical trials published in Clinical Rehabilitation have evolved over three decades with respect to accepted standards of scientific rigour. Studies were identified by journal, database, and hand searching for the years 1986 to 2016.A total of 390 articles whose titles suggested a clinical trial of an intervention, with or without randomization to form groups, were reviewed. Questions often still focused on methods to be used (57%) rather than what knowledge was to be gained. Less than half (43%) of the studies delineated between primary and secondary outcomes; multiple outcomes were common; and sample sizes were relatively small (mean 83, range 5 to 3312). Blinding of assessors was common (72%); blinding of study subjects was rare (19%). In less than one-third of studies was intention-to-treat analysis done correctly; power was reported in 43%. There is evidence of publication bias as 83% of studies reported either a between-group or a within-group effect. Over time, there was an increase in the use of parameter estimation rather than hypothesis testing and there was evidence that methodological rigour improved.Rehabilitation trialists are answering important questions about their interventions. Outcomes need to be more patient-centred and a measurement framework needs to be explicit. More advanced statistical methods are needed as interventions are complex. Suggestions for moving forward over the next decades are given.
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 | 0.892 | 0.957 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.033 | 0.041 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.041 | 0.047 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".