The Global Spine Care Initiative: resources to implement a spine care program
Bibliographic record
Abstract
PURPOSE: The purpose of this report is to describe the development of a list of resources necessary to implement a model of care for the management of spine-related concerns anywhere in the world, but especially in underserved communities and low- and middle-income countries. METHODS: Contents from the Global Spine Care Initiative (GSCI) Classification System and GSCI care pathway papers provided a foundation for the resources list. A seed document was developed that included resources for spine care that could be delivered in primary, secondary and tertiary settings, as well as resources needed for self-care and community-based settings for a wide variety of spine concerns (e.g., back and neck pain, deformity, spine injury, neurological conditions, pathology and spinal diseases). An iterative expert consensus process was used using electronic surveys. RESULTS: Thirty-five experts completed the process. An iterative consensus process was used through an electronic survey. A consensus was reached after two rounds. The checklist of resources included the following categories: healthcare provider knowledge and skills, materials and equipment, human resources, facilities and infrastructure. The list identifies resources needed to implement a spine care program in any community, which are based upon spine care needs. CONCLUSION: To our knowledge, this is the first international and interprofessional attempt to develop a list of resources needed to deliver care in an evidence-based care pathway for the management of people presenting with spine-related concerns. This resource list needs to be field tested in a variety of communities with different resource capacities to verify its utility. These slides can be retrieved under Electronic Supplementary Material.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".