i-CONTENT tool for assessing therapeutic quality of exercise programs employed in randomised clinical trials
Bibliographic record
Abstract
OBJECTIVE: When appraising the quality of randomised clinical trial (RCTs) on the merits of exercise therapy, we typically limit our assessment to the quality of the methods. However, heterogeneity across studies can also be caused by differences in the quality of the exercise interventions (ie, 'the potential effectiveness of a specific intervention given the potential target group of patients')-a challenging concept to assess. We propose an internationally developed, consensus-based tool that aims to assess the quality of exercise therapy programmes studied in RCTs: the international Consensus on Therapeutic Exercise aNd Training (i-CONTENT) tool. METHODS: Forty-nine experts (from 12 different countries) in the field of physical and exercise therapy participated in a four-stage Delphi approach to develop the i-CONTENT tool: (1) item generation (Delphi round 1), (2) item selection (Delphi rounds 2 and 3), (3) item specification (focus group discussion) and (4) tool development and refinement (working group discussion and piloting). RESULTS: Out of the 61 items generated in the first Delphi round, consensus was reached on 17 items, resulting in seven final items that form the i-CONTENT tool: (1) patient selection; (2) qualified supervisor; (3) type and timing of outcome assessment; (4) dosage parameters (frequency, intensity, time); (5) type of exercise; (6) safety of the exercise programme and (7) adherence to the exercise programme. CONCLUSION: The i-CONTENT-tool is a step towards transparent assessment of the quality of exercise therapy programmes studied in RCTs, and ultimately, towards the development of future, higher quality, exercise interventions.
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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 | 0.554 | 0.789 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.035 | 0.026 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".