Mapping the Stroke Impact Scale (SIS-16) to the International Classification of Functioning, Disability and Health
Bibliographic record
Abstract
OBJECTIVE: To demonstrate how the International Classification of Functioning, Disability and Health (ICF) can be used to create coded functional status indicators specific for stroke from a simple stroke-specific functional index, the Stroke Impact Scale-16 (SIS-16). SUBJECTS: Nineteen professionals for the mapping portion and 8 persons with stroke for the cognitive debriefing portion. METHODS: Participants were asked to identify appropriate codes for the corresponding items of the SIS-16 following a structured protocol for mapping measures to the ICF. A Delphi technique was used in order to reach consensus for as many items as possible. In addition, cognitive debriefing was conducted with persons with stroke. RESULTS: A total of 13 items had Functional Status Indicators endorsed (8 items at the 4 digit level and 5 items at the 3 digit level). There were 3 items that did not reach consensus. The cognitive debriefing sessions demonstrated the differences in interpretation from the persons with stroke and the intentions by the developers. CONCLUSION: This study has shown how the ICF can capture most items from functional status measures, such as the SIS-16. Furthermore, the items can be used to map onto a standard coding framework, illustrating the potential for increased use of Functional Status Indicators.
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.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".