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 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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".