Mapping healthcare services and practice variations in spinal care across countries: The Spine Atlas Initiative
Bibliographic record
Abstract
Introduction: Inspired by cancer registration, the Spine Atlas Initiative (SAI) aims to create a global, standardised framework for mapping spine care services and treatment variations across countries. This initiative seeks to improve understanding of spine pathology trends and treatment patterns internationally. Research question: How can a standardised international data collection and visualisation framework enhance the understanding of spine care variations and inform healthcare practices? Material and methods: The SAI will conduct international data calls, focusing on specific spinal pathologies, beginning with lumbar degenerative spondylolisthesis (LDS) in 2025. Participants, including hospitals, registries, and practitioners, will report 7 mandatory and 7 optional data parameters. Data will be collected through templates, the Spine Tango platform, or existing registry formats. Data quality and representativeness will be strictly evaluated to ensure comparability across regions. Results: The 2025 LDS data call has attracted over 280 surgeons from 50 countries, which expressed their interest to participate. The collected data will provide valuable insights into variations in LDS treatment practices and outcomes across different regions. Discussion and conclusion: The SAI offers a collaborative, low-barrier approach to data collection, providing a platform for international research and comparison. The initiative will enhance understanding of treatment variability and outcomes, foster evidence-based improvements in clinical practice, and guide healthcare policy. Future data calls will expand to cover other spinal pathologies and non-surgical treatments, contributing to a global research network and improving spine care worldwide.
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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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".