Spine Intervention—An Update on Injectable Biomaterials
Bibliographic record
Abstract
Back pain is the second most common reason for primary-care physician visits after the common cold. New understanding of the spine pathophysiology and biomechanics led to the development of novel injectable biomaterials to treat those pain generators. Although not all biomaterials are currently ready for common use, there is significant interest by the medical community to invest time, resources, and energy to optimize these injectables. This review introduces basic concepts and advancements in the field of bioinjectables tailored for the vertebral body. Also, we highlight advances in injectable biomaterials which were presented at the Groupe de Recherche Interdisciplinaire sur les Biomatériaux Ostéoarticulaires Injectables (GRIBOI) (Interdisciplinary Research Society for Injectable Osteoarticular Biomaterials) meeting in March 2018 in Los Angeles, CA. Indeed, multidisciplinary translational research and international meetings such as GRIBOI bring together scientists and clinicians with different backgrounds/expertise to discuss injectable biomaterials innovations tailored for the interventional pain management field.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".