Secondary Prevention of Chronic Health Conditions in Patients with Multimorbidity: What can Physiotherapists do?
Bibliographic record
Abstract
Multimorbidity is the co-occurrence of two or more diseases in an individual without a defining index disease [1,2]. In developed countries, the prevalence of multimorbidity has been estimated from both general practice and population data [3,4]. Data from general practices in Scotland found that 23% of patients had multimorbidity [3], whereas the prevalence of multimorbidity in Québec, Canada, was 46–51% in the general practice population and 10–13% in the general population aged over 24 years [4]. Australian data indicate that almost 40% of people aged over 44 years have multimorbidity, and this proportion increases to around 50% of those aged 65–74 years and to 70% of those aged 85 and over [5]. Data from a study of Australian general practice activity reported prevalence estimates for the most common combinations of chronic conditions [6]. Of the 12 most common combinations, the majority included conditions that can be positively impacted by physiotherapy interventions, such as low back pain [7], arthritis [8], chronic obstructive pulmonary disease [9], cardiac disease [10] and type 2 diabetes [11]. However, for some of these conditions, the uptake and access to physiotherapy interventions was suboptimal, especially in the primary care setting, due to poor referral from general practitioners (GPs) [12,13] and/or restricted access to physiotherapy associated with workforce shortages, as well as high cost to the patient for private consultation.\n\nJournal of Comorbidity 2016;6(2):50–52
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".