International Classification of Function, Disability and Health (ICF) Word Mapping to Determine the Human Functioning Associated with Upper Extremity Surgery for Tetraplegia
Bibliographic record
Abstract
Understanding human functioning and disablement, the contributing factors and their interactions in individuals with tetraplegia is important since elective upper extremity (UE) reconstructive surgery is now offered earlier after injury prior to full recognition of what lies ahead. Qualitative and quantitative data were available from a prior series of mixed methods studies, including a case series design capturing the patients’ lived-experience perspectives of nerve or tendon transfer surgery, or not as the case may be. The objective of this study was to perform secondary data analysis to determine whether the recommended outcome tools being used by clinicians reflect the all important domains of functioning identified by people with tetraplegia who were considering UE reconstructive procedures. The original 18 candidate themes derived from qualitative analysis were reviewed in retrospect, along with a content analysis of the tools’ questions, undertaking word mapping links to the ICF taxonomy. The outcomes tools included in the content analysis were the Canadian Occupational Performance Measure, the Capabilities of Upper Extremity Questionnaire, The Personal Wellbeing Index, and the Grasp and Release Test. Comparison between clinical outcomes tools and the patient lived-experience data uniquely identified links to Chapter1 (b) Mental functions, which include consciousness, orientation, temperament/personality, energy/drive, and higher-level cognition.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".