i-CONTENT tool for assessing therapeutic quality of exercise programs employed in randomised clinical trials
Why this work is in the frame
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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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it