Identification of Tools for the Functional and Subjective Assessment of Patients in an Aquatic Environment: A Systematic Review
Bibliographic record
Abstract
Aquatic therapy is one of the most common treatments for alleviating musculoskeletal pathologies. Its effectiveness has been evaluated with functional tests and questionnaires. Functional tests are used in aquatic therapy; however, in most cases, they are carried out in a non-aquatic environment and, as such, their results may differ from those of tests performed in an aquatic environment. A systematic review was performed to assess the accuracy of functional tests and patient-reported outcomes to assess aquatic therapy interventions. The authors conducted a literature search in July 2019. In total, 70,863 records were identified after duplicates removed. Of these, 14 records were included about functional tests assessment in aquatic environment and 725 records for questionnaires. The majority of the tests had also been assessed in a dry environment, allowing differences and similarities between the tests in the two environments to be observed. Different variables have been assessed in tests included in the present systematic review (cardiorespiratory, neuromuscular, kinematic, physiological, kinetic responses and rating of perceived exertion) which are included in the manuscript. Visual Analogue Scale, Western Ontario and McMaster Universities Osteoarthritis Index and the 12-item Short Form Health Survey were the assessments most commonly used by the different authors.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".