SPINE20 recommendations 2021: spine care for people’s health and prosperity
Bibliographic record
Abstract
PURPOSE: The focus of SPINE20 is to develop evidence-based policy recommendations for the G20 countries to work with governments to reduce the burden of spine disease, and disability. METHODS: On September 17-18, 2021, SPINE20 held its annual meeting in Rome, Italy. Prior to the meeting, the SPINE20 created six proposed recommendations. These recommendations were uploaded to the SPINE20 website 10 days before the meeting and opened to the public for comments. The recommendations were discussed at the meeting allowing the participants to object and provide comments. RESULTS: In total, 27 societies endorsed the following recommendations. SPINE20 calls upon the G20 countries: (1) to expand telehealth for the access to spine care, especially in light of the current situation with COVID-19. (2) To adopt value-based interprofessional spine care as an approach to improve patient outcomes and reduce disability. (3) To facilitate access and invest in the development of a competent rehabilitation workforce to reduce the burden of disability related to spine disorders. (4) To adopt a strategy to promote daily physical activity and exercises among the elderly population to maintain an active and independent life with a healthy spine, particularly after COVID-19 pandemic. (5) To engage in capacity building with emerging countries and underserved communities for the benefit of spine patients. (6) To promote strategies to transfer evidence-based advances into patient benefit through effective implementation processes. CONCLUSIONS: SPINE20's initiatives will make governments and decision makers aware of efforts to reduce needless suffering from disabling spine pain through education that can be instituted across the globe.
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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 | 0.030 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.031 | 0.018 |
| Insufficient payload (model declined to judge) | 0.058 | 0.049 |
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".