Multimorbidity:A complex reality in primary health care
Bibliographic record
Abstract
The focus of primary health care (PHC) in developed countries is now largely centred on the treatment and management of long-term or chronic diseases. Due to shared risk factors and interaction among diseases, chronic conditions are increasingly occurring in clusters.1 In Canada, more than 50% of adults aged 65 years and older report having at least two chronic diseases.2 The co-occurrence of multiple chronic diseases in an individual, or multimorbidity, is also understood to be the norm rather than the exception in PHC.3 Multimorbidity is associated with reduced quality of life, limited functional status, polypharmacy, increased mortality, and high health care costs.3 Deemed an “endless struggle” by PHC providers, multimorbidity is becoming more prevalent in younger patients and is no longer confined to elderly populations. 1,4 This phenomenon is pushing PHC providers and researchers alike to understand its multifaceted nature. A better understanding of the etiology behind multimorbidity can lead to a transformed clinical approach that will, in turn, be cost-saving in the long-run. To achieve this, three main components are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".