Bibliographic record
Abstract
Multimorbidity is common. The gaps in multimorbidity research are in the measurement of the prevalence, the levels of multimorbidity and its associated outcomes.\nThis thesis aimed to provide a uniform definition for multimorbidity, identify instruments for measuring the level of multimorbidity, and describe patient-reported outcomes for different levels of multimorbidity.\nThree studies were conducted. The first determined the prevalence rates of multimorbidity and explored whether there were differences among the different age, gender and ethnic groups in the primary care population. Common dyads and triads of conditions were described. The systematic review updated the list of instruments for measuring the level of multimorbidity for community-dwelling adults. The third study determined the association of different levels of multimorbidity with depression, anxiety and quality of life. The agreement between patients’ self-reported conditions and conditions recorded in their electronic medical records (EMR) were reported.\nIncreasing age was associated with a higher prevalence of multimorbidity. The commonest dyad was hyperlipidaemia/hypertension, and triad was hyperlipidaemia/hypertension/diabetes. Disease count and weighted indices were the most commonly used instruments for measuring the level of multimorbidity. Self-reported disease count was positively associated with depression and anxiety, and negatively associated with quality of life. Stroke was the only condition that showed substantial agreement between patients’ self-reported medical conditions and the EMR.\nWe identified a practical definition of multimorbidity in the Singapore primary care population, described the commonly used instruments for measuring the level of multimorbidity, and reported the disparity of multimorbidity outcomes between patients’ self-reported chronic conditions and EMR.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".