An ICF-based assessment schedule to facilitate the assessment and reporting of functioning in manual medicine – low back pain as a case in point
Bibliographic record
Abstract
PURPOSE: This paper outlines the first steps toward developing the ICF-based assessment schedule for manual medicine with a focus on low back pain (LBP). It reports on the results of a consensus process to develop the default and optional versions of the set of ICF categories (ManMed Set) the assessment schedule should cover, and gives insight in expert input toward building a toolbox of instruments for assessing the ManMed Set categories. METHODS: A scoping review and qualitative study were conducted, each resulting in a list of ICF categories. These categories, along with the categories of the ICF Generic-30 Set, Comprehensive ICF Core Set for LBP, and from an existing Delphi study, served as the starting point for an established consensus process to decide on the ManMed Set. RESULTS: After alternating plenary and working group sessions, an iterative ranking process and cut-off calculation, the multi-professional and international group of 20 experts in manual medicine included 23 categories in the default ManMed version (16 + the ICF Generic-7 Set categories) and 25 in the optional version. CONCLUSIONS: Their development is a major step toward developing an assessment schedule that can be employed in standardizing the assessment and reporting of functioning in manual medicine, initially of LBP patients.Implications for rehabilitationThe ICF assessment schedule for manual medicine has potential use in supporting rehabilitation practice, such as for planning interventions, defining rehabilitation goals, and measuring and documenting functioning outcomes.It can be used to promote interdisciplinary coordination of care and facilitate communication between members of a multidisciplinary rehabilitation team within manual medicine and beyond.The ICF assessment schedule for manual medicine can facilitate rehabilitation and manual medicine research by providing evidence for optimizing rehabilitation practice.
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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 | 0.070 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".