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Record W4405586977 · doi:10.3390/cells13242103

Intervertebral Disc Degeneration and Regeneration: New Molecular Mechanisms and Therapeutics: Obstacles and Potential Breakthrough Technologies

2024· review· en· W4405586977 on OpenAlexaff
William T. Taylor, William Mark Erwin

Bibliographic record

VenueCells · 2024
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDegeneration (medical)Compartmentalization (fire protection)Degenerative disc diseaseMedicineIntervertebral discModalitiesRegeneration (biology)DiseaseLow back painClinical PracticeNeurosciencePathologyPhysical therapyPsychologySurgeryBiologyAlternative medicine

Abstract

fetched live from OpenAlex

Pain and disability secondary to degenerative disc disease continue to burden the healthcare system, creating an urgent need for effective, disease-modifying therapies. Contemporary research has identified potential therapies that include protein-, cellular- and/or matrix-related approaches; however, none have yet achieved a meaningful clinical impact. The tissue-specific realities of the intervertebral disc create considerable therapeutic challenges due to the disc's location, compartmentalization, hypovascularization and delicate physiological environment. Furthermore, the imaging modalities currently used in practice are largely unable to accurately identify sources of pain ostensibly discogenic in origin. These obstacles are considerable; however, recent research has begun to shed light on possible breakthrough technologies. Such breakthroughs include revolutionary imaging to better identify tissue sources of pain. Furthermore, novel molecular therapies have been shown to be able to mediate the progression of degenerative disc disease in some large animal studies, and even provide some insight into suppressing the development of tissue sources of discogenic pain. These potential breakthrough technologies have yet to be translated for clinical use.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.321
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations21
Published2024
Admission routes1
Has abstractyes

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