Using the Functional Comorbidity Index with administrative workers’ compensation data: Utility, validity, and caveats
Bibliographic record
Abstract
BACKGROUND: Chronic health conditions impact worker outcomes but are challenging to measure using administrative workers' compensation (WC) data. The Functional Comorbidity Index (FCI) was developed to predict functional outcomes in community-based adult populations, but has not been validated for WC settings. We assessed a WC-based FCI (additive index of 18 conditions) for identifying chronic conditions and predicting work outcomes. METHODS: WC data were linked to a prospective survey in Ohio (N = 512) and Washington (N = 2,839). Workers were interviewed 6 weeks and 6 months after work-related injury. Observed prevalence and concordance were calculated; survey data provided the reference standard for WC data. Predictive validity and utility for control of confounding were assessed using 6-month work-related outcomes. RESULTS: The WC-based FCI had high specificity but low sensitivity and was weakly associated with work-related outcomes. The survey-based FCI suggested more comorbidity in the Ohio sample (Ohio mean = 1.38; Washington mean = 1.14), whereas the WC-based FCI suggested more comorbidity in the Washington sample (Ohio mean = 0.10; Washington mean = 0.33). In the confounding assessment, adding the survey-based FCI to the base model moved the state effect estimates slightly toward null (<1% change). However, substituting the WC-based FCI moved the estimate away from null (8.95% change). CONCLUSIONS: The WC-based FCI may be useful for identifying specific subsets of workers with chronic conditions, but less useful for chronic condition prevalence. Using the WC-based FCI cross-state appeared to introduce substantial confounding. We strongly advise caution-including state-specific analyses with a reliable reference standard-before using a WC-based FCI in studies involving multiple states.
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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 | 0.167 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".