How Does the Measurement of Disability in Low Back Pain Map Unto the International Classification of Functioning, Disability and Health?
Bibliographic record
Abstract
ABSTRACT: The objective of this study was to catalog items from instruments used to measure functioning, disability, and contextual factors in patients with low back pain treated with manual medicine (manipulation and mobilization) according to the International Classification of Functioning, Disability and Health. This catalog will be used to inform the development of an International Classification of Functioning, Disability and Health-based assessment schedule for low back pain patients treated with manual medicine. In this scoping review, we systematically searched MEDLINE, Embase, PsycINFO, and CINAHL. We identified instruments (questionnaires, clinical tests, single questions) used to measure functioning, disability, and contextual factors, extracted the relevant items, and then linked these items to the International Classification of Functioning, Disability and Health. We included 95 articles and identified 1510 meaningful concepts. All but 70 items were linked to the International Classification of Functioning, Disability and Health. Of the concepts linked to the International Classification of Functioning, Disability and Health, body functions accounted for 34.7%, body structures accounted for 0%, activities and participation accounted for 41%, environmental factors accounted for 3.6%, and personal factors accounted for 16%. Most items used to measure functioning and disability in low back pain patient treated with manual medicine focus on body functions, as well as activities and participation. The lack of measures that address environmental factors warrants further investigation.
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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 | 0.049 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".