Bibliographic record
Abstract
Physiotherapy remains the cornerstone of cystic fibrosis (CF) management alongside medical treatment. Traditionally, physiotherapy intervention focussed on airway clearance during the clinically stable stage and chest infections. Research evidence consistently supports greater mucus clearance with chest physiotherapy compared to cough alone or no treatment. Various methods and techniques of airway clearance have been developed and investigated, and data suggest that most of them are of similar effectiveness. Nowadays physiotherapy management also extends to other areas, supported by studies and clinical practice. The physiotherapists plan, supervise and follow-up systematic exercise or personalised rehabilitation programs, which, similarly to airway clearance, are recommended in all patients with CF. Furthermore, based on a comprehensive assessment, physiotherapists incorporate the management of accompanying musculoskeletal problems such as back pain and postural disorders, as well as urine incontinence issues. In the era that aims to improve quality of life, it is essential that physiotherapists are aware of specific conditions that might affect the management of CF. Their role is to work alongside and within the CF multi-disciplinary team throughout patient’s treatment and consistently support the patient and carers, in particular whilst on clinical pathways of the lung transplantation and palliative care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".