Defining the phenotype of young healthy nucleus pulposus cells: Recommendations of the Spine Research Interest Group at the 2014 annual ORS meeting
Bibliographic record
Abstract
Low back pain is a major physical and socioeconomic problem. Degeneration of the intervertebral disc and especially that of nucleus pulposus (NP) has been linked to low back pain. In spite of much research focusing on the NP, consensus among the research community is lacking in defining the NP cell phenotype. A consensus agreement will allow easier distinguishing of NP cells from annulus fibrosus (AF) cells and endplate chondrocytes, a better gauge of therapeutic success, and a better guidance of tissue-engineering-based regenerative strategies that attempt to replace lost NP tissue. Most importantly, a clear definition will further the understanding of physiology and function of NP cells, ultimately driving development of novel cell-based therapeutic modalities. The Spine Research Interest Group at the 2014 Annual ORS Meeting in New Orleans convened with the task of compiling a working definition of the NP cell phenotype with hope that a consensus statement will propel disc research forward into the future. Based on evaluation of recent studies describing characteristic NP markers and their physiologic relevance, we make the recommendation of the following healthy NP phenotypic markers: stabilized expression of HIF-1α, GLUT-1, aggrecan/collagen II ratio >20, Shh, Brachyury, KRT18/19, CA12, and CD24.
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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 | 0.055 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".