A roadmap to surgery in osteogenesis imperfecta: results of an international collaboration of patient organizations and interdisciplinary care teams
Bibliographic record
Abstract
Background and purpose - Involvement of patient organizations is steadily increasing in guidelines for treatment of various diseases and conditions for better care from the patient's viewpoint and better comparability of outcomes. For this reason, the Osteogenesis Imperfecta Federation Europe and the Care4BrittleBones Foundation convened an interdisciplinary task force of 3 members from patient organizations and 12 healthcare professionals from recognized centers for interdisciplinary care for children and adults with osteogenesis imperfecta (OI) to develop guidelines for a basic roadmap to surgery in OI.Methods - All information from 9 telephone conferences, expert consultations, and face-to-face meetings during the International Conference for Quality of Life for Osteogenesis Imperfecta 2019 was used by the task force to define themes and associated recommendations.Results - Consensus on recommendations was reached within 4 themes: the interdisciplinary approach, the surgical decision-making conversation, surgical technique guidelines for OI, and the feedback loop after surgery.Interpretation - The basic guidelines of this roadmap for the interdisciplinary approach to surgical care in children and adults with OI is expected to improve standardization of clinical practice and comparability of outcomes across treatment centers.
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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 | 0.139 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".