The functional comorbidity index had high inter-rater reliability in patients with acute lung injury
Bibliographic record
Abstract
BACKGROUND: The Functional Comorbidity Index (FCI) was recently developed to predict physical function in acute lung injury patients using comorbidity data. Our objectives were to determine: (1) the inter-rater reliability of the FCI collected using in-patient discharge summaries (primary objective); and (2) the accuracy and predictive validity of the FCI collected using hospital discharge summaries and admission records versus complete chart review (secondary objectives). METHODS: For reliability, we evaluated the FCI's intraclass correlation coefficient (ICC) among trained research staff performing data collection for 421 acute lung injury patients enrolled in a prospective cohort study. For validity and accuracy, we compared the detection of FCI comorbidities across three types of inpatient medical records, and the association of the respective FCI scores obtained with patients' SF-36 physical function subscale (PFS) scores at 1-year follow-up. RESULTS: Inter-rater reliability was near-perfect (ICC 0.91; 95% CI 0.89-0.94). Hospital admission records and discharge summaries (vs. complete chart review) significantly underestimated the total FCI score. However, using multivariable linear regression, FCI scores collected using each of the three types of inpatient medical records had similar associations with PFS, suggesting similar predictive value. CONCLUSIONS: Data collection using in-patient discharge summaries represents a reliable and valid method for collecting FCI comorbidity information.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".