Development and validation of the Cambridge Multimorbidity Score
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Health services have failed to respond to the pressures of multimorbidity. Improved measures of multimorbidity are needed for conducting research, planning services and allocating resources. METHODS: = 150 000) samples from the UK Clinical Practice Research Datalink. We constructed a general-outcome multimorbidity score by averaging the standardized weights of the separate outcome scores. We compared performance with the Charlson Comorbidity Index. RESULTS: Models that included all 37 conditions were acceptable predictors of general practitioner consultations (C-index 0.732, 95% confidence interval [CI] 0.731-0.734), unplanned hospital admission (C-index 0.742, 95% CI 0.737-0.747) and death at 1 year (C-index 0.912, 95% CI 0.905-0.918). Models reduced to the 20 conditions with the greatest combined prevalence/weight showed similar predictive ability (C-indices 0.727, 95% CI 0.725-0.728; 0.738, 95% CI 0.732-0.743; and 0.910, 95% CI 0.904-0.917, respectively). They also predicted 5-year outcomes similarly for consultations and death (C-indices 0.735, 95% CI 0.734-0.736, and 0.889, 95% CI 0.885-0.892, respectively) but performed less well for admissions (C-index 0.708, 95% CI 0.705-0.712). The performance of the general-outcome score was similar to that of the outcome-specific models. These models performed significantly better than those based on the Charlson Comorbidity Index for consultations (C-index 0.691, 95% CI 0.690-0.693) and admissions (C-index 0.703, 95% CI 0.697-0.709) and similarly for mortality (C-index 0.907, 95% CI 0.900-0.914). INTERPRETATION: The Cambridge Multimorbidity Score is robust and can be either tailored or not tailored to specific health outcomes. It will be valuable to those planning clinical services, policymakers allocating resources and researchers seeking to account for the effect of multimorbidity.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it