Strengthening the backbone of global spine surgery
Bibliographic record
Abstract
Strengthening the backbone of global spine surgerySpinal disorders contribute to significant disability and burden for affected individuals and society worldwide.The Lancet commission on global burden of disease in 2019 identified low back pain as one of the top causes of disability in the adult population (aged 25-49 years old), causing disability and loss of function in more than half a billion individuals (GBD, 2019 Diseases and Injuries Collaborators, 2020).Fortunately, over the last decade, giant leaps in neurosurgical care have contributed to numerous advances that enable safe, effective and efficient surgery of the brain and spine.As it stands today, the modern practice of spine surgery has catapulted into one of the most dynamic and innovative specialties in surgery due to the collective and collaborative effort of many neurosurgeon and orthopedic specialists across the globe.The benefits of these innovations, however, have trickled disproportionately across nations.Currently, many patients from low and middle-income countries (LMICs) still lack access to essential neurosurgical care, and this reality brings into focus several important issues that transcend the realms of equitable and quality health care for all.The series of papers (Marchesini et al., 2022a(Marchesini et al., , 2022b;;Demetriades et al., 2022) recently published in Brain & Spine's inaugural special issue on Global and Humanitarian Neurosurgery discuss the management of traumatic spinal cord injury (SCI) in LMICs and serve to highlight deficiencies in the delivery of this aspect of neurosurgical care as well as to identify the spatial geographic locations where this continues to engender suboptimal patient care.In many high-income countries, the provision of basic and advanced spine surgery and anesthesia is considered an essential component of standard of care.However, in developing countries, poor access to affordable and safe spine surgical care remains a major cause of mortality and morbidity, impacting approximately five billion people and representing over one third of the global burden of disease (Meara et al., 2015).As a result, the survival difference continues to widen between high income countries (HICs) and LMICs.The seminal three-part paper serves to provide a contextualized understanding of the unmet spinal surgery needs by showcasing the current existing ground-level realities in many LMICs.We believe that these studies satisfy the urgent need for a pragmatic description of the actual deficiencies in spine trauma care, and therefore represent an important initial step towards developing potential strategies to close the existing worldwide gap.In recent years, key global and international neurosurgical organizations increased collaboration to improve access and delivery of neurosurgical care, particularly in LMICs; giving rise to what has been termed the global neurosurgery movement.The origin of this crusade dates back to 1980 when the former WHO Director General Halfdan T.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".