The Global Spine Care Initiative: methodology, contributors, and disclosures
Bibliographic record
Abstract
PURPOSE: The purpose of this report is to describe the Global Spine Care Initiative (GSCI) contributors, disclosures, and methods for reporting transparency on the development of the recommendations. METHODS: World Spine Care convened the GSCI to develop an evidence-based, practical, and sustainable healthcare model for spinal care. The initiative aims to improve the management, prevention, and public health for spine-related disorders worldwide; thus, global representation was essential. A series of meetings established the initiative's mission and goals. Electronic surveys collected contributorship and demographic information, and experiences with spinal conditions to better understand perceptions and potential biases that were contributing to the model of care. RESULTS: Sixty-eight clinicians and scientists participated in the deliberations and are authors of one or more of the GSCI articles. Of these experts, 57 reported providing spine care in 34 countries, (i.e., low-, middle-, and high-income countries, as well as underserved communities in high-income countries.) The majority reported personally experiencing or having a close family member with one or more spinal concerns including: spine-related trauma or injury, spinal problems that required emergency or surgical intervention, spinal pain referred from non-spine sources, spinal deformity, spinal pathology or disease, neurological problems, and/or mild, moderate, or severe back or neck pain. There were no substantial reported conflicts of interest. CONCLUSION: The GSCI participants have broad professional experience and wide international distribution with no discipline dominating the deliberations. The GSCI believes this set of papers has the potential to inform and improve spine care globally. 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 | 0.000 | 0.001 |
| 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".