The electronic Cumulative Illness Rating Scale: a reliable and valid tool to assess multi-morbidity in primary care
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The presence of multiple concomitant diseases is an increasing health problem, and prompted by the limitations of the disease count, several indices measuring multi-morbidity or co-morbidity have been described to account for the overall burden of morbidity. The Cumulative Illness Rating Scale (CIRS) is one of those indices. We developed an electronic version of the CIRS (eCIRS) to take advantage of computerized data processing. The aim of this study was to evaluate the reliability and validity of the eCIRS scored in a primary care setting. METHODS: Two nurses interviewed 48 adult patients recruited during consecutive consultation periods in a primary care setting and scored the eCIRS in a random order during two sessions of data collection (T1 and T2) 1 month apart. We measured intra- and inter-rater reliability [intra-class correlation coefficient (ICC)]. We also assessed concomitant validity [(Pearson's correlation (r)] using standard CIRS scored by the attending family doctors. RESULTS: Intra-rater (ICC: 0.90 and 0.95) and inter-rater reliability (ICC: 0.86 and 0.91) were both excellent. No significant differences between the nurses' scores at T1 and T2 (P = 0.40 for nurse 1, P = 0.73 for nurse 2) were found. The eCIRSs scored by the nurses were highly correlated with the CIRSs scored by the doctors (r = 0.80 and 0.88). CONCLUSION: Reliable and valid, the eCIRS completed during patient interviews with trained nurses can be used to quantify multi-morbidity in primary care, either for research or clinical use.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".