The TIDieR Checklist Will Benefit the Physiotherapy Profession
Bibliographic record
Abstract
[Extract] Evidence-based practice involves physiotherapists incorporating high-quality clinical research on treatment efficacy into their clinical decision making. However, if clinical interventions are not adequately reported in the literature, physiotherapists face an important barrier to using effective interventions with their patients. Previous studies have reported that incomplete description of interventions is a problem in reports of randomized controlled trials in many health areas. One of these studies examined 133 trials of non-pharmacological interventions. The experimental intervention was inadequately described in more than 60% of the trials, and descriptions of the control interventions were even worse. A recent study evaluated the completeness of descriptions of the physiotherapy interventions in a sample of 200 randomized controlled trials published in 2013. Overall, the interventions were poorly described. For the intervention groups, about one-quarter of the trials did not fulfil at least half of the criteria. Reporting for the control groups was even worse, with around three-quarters of trials not fulfilling at least half of the criteria. In other words, for the majority of the physiotherapy trials, clinicians and researchers would be unable to replicate the interventions that were tested. Describing a treatment may seem like a simple task, but physiotherapy interventions can be very complex. Some interventions are multi-modal, involving the use of manual techniques, consumable materials, equipment, education, training, and feedback. Some interventions are tailored to each patient's specific health state, including the patient's immediate response to the application of the treatment. When the intervention involves a course of treatments, the intensity or dose may be progressed over time. The descriptions of physiotherapy interventions in trial reports often do not capture all these components of the interventions or detail their complexity.
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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 | 0.036 | 0.182 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.057 | 0.042 |
| Insufficient payload (model declined to judge) | 0.042 | 0.032 |
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".