The Global Spine Care Initiative: model of care and implementation
Bibliographic record
Abstract
Spine-related disorders are a leading cause of global disability and are a burden on society and to public health. Currently, there is no comprehensive, evidence-based model of care for spine-related disorders, which includes back and neck pain, deformity, spine injury, neurological conditions, spinal diseases, and pathology, that could be applied in global health care settings. The purposes of this paper are to propose: (1) principles to transform the delivery of spine care; (2) an evidence-based model that could be applied globally; and (3) implementation suggestions.The Global Spine Care Initiative (GSCI) meetings and literature reviews were synthesized into a seed document and distributed to spine care experts. After three rounds of a modified Delphi process, all participants reached consensus on the final model of care and implementation steps.Sixty-six experts representing 24 countries participated. The GSCI model of care has eight core principles: person-centered, people-centered, biopsychosocial, proactive, evidence-based, integrative, collaborative, and self-sustaining. The model of care includes a classification system and care pathway, levels of care, and a focus on the patient's journey. The six steps for implementation are initiation and preparation; assessment of the current situation; planning and designing solutions; implementation; assessment and evaluation of program; and sustain program and scale up.The GSCI proposes an evidence-based, practical, sustainable, and scalable model of care representing eight core principles with a six-step implementation plan. The aim of this model is to help transform spine care globally, especially in low- and middle-income countries and underserved communities. These slides can be retrieved under Electronic Supplementary Material.
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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 | 0.167 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".