SPINE20 recommendations 2023: One Earth, one family, one future WITHOUT spine DISABILITY
Bibliographic record
Abstract
Introduction: The purpose is to report on the fourth set of recommendations developed by SPINE20 to advocate for evidence-based spine care globally under the theme of "One Earth, One Family, One Future WITHOUT Spine DISABILITY". Research question: Not applicable. Material and methods: Recommendations were developed and refined through two modified Delphi processes with international, multi-professional panels. Results: Seven recommendations were delivered to the G20 countries calling them to:-establish, prioritize and implement accessible National Spine Care Programs to improve spine care and health outcomes.-eliminate structural barriers to accessing timely rehabilitation for spinal disorders to reduce poverty.-implement cost-effective, evidence-based practice for digital transformation in spine care, to deliver self-management and prevention, evaluate practice and measure outcomes.-monitor and reduce safety lapses in primary care including missed diagnoses of serious spine pathologies and risk factors for spinal disability and chronicity.-develop, implement and evaluate standardization processes for spine care delivery systems tailored to individual and population health needs.-ensure accessible and affordable quality care to persons with spine disorders, injuries and related disabilities throughout the lifespan.-promote and facilitate healthy lifestyle choices (including physical activity, nutrition, smoking cessation) to improve spine wellness and health. Discussion and conclusion: SPINE20 proposes that focusing on the recommendations would facilitate equitable access to health systems, affordable spine care delivered by a competent healthcare workforce, and education of persons with spine disorders, which will contribute to reducing spine disability, associated poverty, and increase productivity of the G20 nations.
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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 | 0.035 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.020 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".