Incorporating the International Classification of Functioning, Disability, and Health (ICF) into an Electronic Health Record to Create Indicators of Function: Proof of Concept Using the SF-12
Bibliographic record
Abstract
OBJECTIVE: The purpose of this proof-of-concept study was to assess the feasibility of using a generic health measure to create coded functional status indicators and compare the characterization of a stroke population using coded functional indicators and using health-related quality-of-life summary measures alone. DESIGN: Multiple raters assigned International Classification of Functioning, Disability, and Health (ICF) codes to the items of the 12-Item Short Form Health Survey (SF-12). Data for comparing the information from the SF-12 and from ICF codes were derived from the Montreal Stroke Cohort Study that was set up to examine the long-term impact of stroke. Available for analysis were data from 604 persons with stroke, average age 69 years, and 488 controls, average age 62 years. MEASUREMENT: The SF-12 provides two summary scores, one for physical health and one for mental health. Domains of the ICF are coded to three digits, before the decimal; specific categorizations of impairments, activity limitations, and participation restrictions are coded to four digits before the decimal. RESULTS: Persons with stroke scored, on average, approximately 10 points lower than controls on physical and mental health. The ICF coding indicated that this was attributed, not surprisingly, to greater difficulty in doing moderate activities including housework, climbing stairs, and working and was not attributed to differences in pain. Differences in mental health were attributed most strongly to greater fatigue (impairment in energy), but all areas of mental health were affected to some degree. CONCLUSION: The ICF coding provided enhanced functional status information in a format compatible with the structure of administrative health databases.
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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 | 0.064 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".