Leaping the hurdles in developing regenerative treatments for the intervertebral disc from preclinical to clinical
Bibliographic record
Abstract
Chronic back and neck pain is a prevalent disability, often caused by degeneration of the intervertebral disc. Because current treatments for this condition are less than satisfactory, a great deal of effort is being applied to develop new solutions, including regenerative strategies. However, the path from initial promising idea to clinical use is fraught with many hurdles to overcome. Many of the keys to success are not necessarily linked to science or innovation. Successful translation to clinic will also rely on planning and awareness of the hurdles. It will be essential to plan your entire path to clinic from the outset and to do this with a multidisciplinary team. Take advice early on regulatory aspects and focus on generating the proof required to satisfy regulatory approval. Scientific demonstration and societal benefits are important, but translation cannot occur without involving commercial parties, which are instrumental to support expensive clinical trials. This will only be possible when intellectual property can be protected sufficiently to support a business model. In this manner, commercial, societal, medical, and scientific partners can work together to ultimately improve patient health. Based on literature surveys and experiences of the co-authors, this opinion paper presents this pathway, highlights the most prominent issues and hopefully will aid in your own translational endeavors.
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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 | 0.051 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".