SPINE20 Recommendations 2024 -Spinal Disability: Social Inclusion as a Key to Prevention and Management
Bibliographic record
Abstract
Spine disorders are the leading cause of disability worldwide. To promote social inclusion, it is essential to ensure that people can participate in their societies by improving their ability, opportunities, and dignity, through access to high-quality, evidence-based, and affordable spine services for all.To achieve this goal, SPINE20 recommends six actions.- SPINE20 recommends that G20 countries deliver evidence-based education to the community health workers and primary care clinicians to promote best practice for spine health, especially in underserved communities.- SPINE20 recommends that G20 countries deliver evidence-based, high-quality, cost-effective spine care interventions that are accessible, affordable and beneficial to patients.- SPINE20 recommends that G20 countries invest in Health Policy and System Research (HPSR) to generate evidence to develop and implement policies aimed at integrating rehabilitation in primary care to improve spine health.- SPINE20 recommends that G20 countries support ongoing research initiatives on digital technologies including artificial intelligence, regulate digital technologies, and promote evidence-based, ethical digital solutions in all aspects of spine care, to enrich patient care with high value and quality.- SPINE20 recommends that G20 countries prioritize social inclusion by promoting equitable access to comprehensive spine care through collaborations with healthcare providers, policymakers, and community organizations.- SPINE20 recommends that G20 countries prioritize spine health to improve the well-being and productivity of their populations. Government health systems are expected to create a healthier, more productive, and equitable society for all through collaborative efforts and sustained investment in evidence-based care and promotion of spine health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.046 | 0.017 |
| Insufficient payload (model declined to judge) | 0.081 | 0.084 |
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".