Developing core sets for patients with obstetric brachial plexus injury based on the International Classification of Functioning, Disability and Health
Bibliographic record
Abstract
BACKGROUND: Symptoms of obstetric brachial plexus injury (OBPI) vary widely over the course of time and from individual to individual and can include various degrees of denervation, muscle weakness, contractures, bone deformities and functional limitations. To date, no universally accepted overall framework is available to assess the outcome of patients with OBPI. The objective of this paper is to outline the proposed process for the development of International Classification of Functioning, Disability and Health (ICF) Core Sets for patients with an OBPI. METHODS: The first step is to conduct four preparatory studies to identify ICF categories important for OBPI: a) a systematic literature review to identify outcome measures, b) a qualitative study using focus groups, c) an expert survey and d) a cross-sectional, multicentre study. A first version of ICF Core Sets will be defined at a consensus conference, which will integrate the evidence from the preparatory studies. In a second step, field-testing among patients will validate this first version of Core Sets for OBPI. DISCUSSION: The proposed method to develop ICF Core Sets for OBPI yields a practical tool for multiple purposes: for clinicians to systematically assess and evaluate the individual's functioning, for researchers to design and compare studies, and for patients to get more insight into their health problems and their management.
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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 | 0.062 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".