Evidence map›Paper›PMID 38156062›Full record

ReviewJOR spine2023

Biomarkers for intervertebral disc and associated back pain: From diagnosis to disease prognosis and personalized treatment.

Catarina Leite Pereira, Sibylle Grad, Raquel M Gonçalves

Open access · goldAbstract readReview
In one paragraph

Review in JOR spine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Regenerative strategies for intervertebral disc degeneration.Journal of orthopaedic translation · 2025
    Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Catarina Leite PereiraI3S, Instituto de Investigação e Inovação em Saúde Universidade do Porto Porto Portugal.
Sibylle GradAO Research Institute Davos Davos Switzerland.ORCID https://orcid.org/0000-0001-9552-3653
Raquel M GonçalvesI3S, Instituto de Investigação e Inovação em Saúde Universidade do Porto Porto Portugal.ORCID https://orcid.org/0000-0003-2841-0485
Universidade do Porto · PTAO Foundation · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomarkers are commonly recognized as objective indicators of a medical state or clinical outcome and have been widely used as clinical and diagnostic tools and surrogate endpoints in many pathological conditions. In the context of intervertebral disc (IVD) and associated back pain, also known as degenerative disc disease (DDD), the use of biomarkers has been poorly explored. DDD is currently diagnosed using imaging techniques and subjective pain scales, limiting an objective association between DDD and pain levels, as well as an evaluation of disease progression. There is a need for objective and reliable measurements for DDD, pain and pathology progression. DDD predictors could also help clinicians in deciding on the optimal treatment for distinct patient groups. This review addresses the current candidate biomarkers in DDD, including imaging, genetic, metabolite and protein-based parameters, both at the tissue and systemic levels, that may become a major advance in the diagnosis and prognosis of the disease, as well as in the management of therapeutic approaches to DDD.

Indexed as

back painbiomarkersdegenerative disc diseasediagnosis

Identifiers

PMID38156062
PMCPMC10751979
OpenAlexW4387297431

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.