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Record W4406128331 · doi:10.3389/fphar.2024.1501590

The efficacy of core decompression combined with regenerative therapy in early femoral head necrosis: a systematic review and meta-analysis involving 954 subjects

2025· review· en· W4406128331 on OpenAlexaboutno aff
Haiwei Tang, Tingxian Ling, Enze Zhao, Mingke You, Xi Chen, Gang Chen, Kai Zhou, Zongke Zhou

Bibliographic record

VenueFrontiers in Pharmacology · 2025
Typereview
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsnot available
FundersWest China Hospital, Sichuan UniversityNational Key Research and Development Program of ChinaHealth Commission of Sichuan ProvinceSichuan UniversityNational Natural Science Foundation of China
KeywordsMeta-analysisMedicineFemoral headDecompressionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background The debate continues on whether combining core decompression (CD) with regenerative therapy provides a more effective treatment for early femoral head necrosis than CD alone. This systematic review and meta-analysis endeavored to assess its efficacy. Methods We systematically searched PubMed, Web of Science, and Cochrane Library through July 2024 for RCTs and cohort studies evaluating the impact of core decompression (CD) with regenerative therapy versus CD alone in early-stage osteonecrosis (ARCO I, II or IIIa or Ficat I or II) of the femoral head (ONFH). Bias was evaluated using the Cochrane ROB 2.0 for RCTs and the Newcastle-Ottawa Scale (NOS) for cohort studies. The primary outcome was disease progression, measured by the incidence of staging advancement and total hip arthroplasty (THA) conversion. Clinical outcomes, including VAS, HHS, WOMAC, and Lequesne index, were secondary measures. Subgroup analyses were performed for variables such as age, BMI, follow-up period, and dosage in the bone marrow aspirate concentrate (BMAC) group, with results depicted in forest plots. Results This study represented a total of seven RCTs (mean follow-up time 36.57 months) and eight cohort trials (mean follow-up time 74.18 months) involving 954 hips. CD, when combined with agents, exhibited considerably enhanced efficacy over CD alone (risk ratio (RR) = 0.55 (95% CI 0.39–0.77), p < 0.001, I2 = 54%) and 0.59 (95% CI 0.43–0.81), p = 0.001, I2 = 51%), respectively). However, a significant difference was exclusive to the CD combined with BMAC group in terms of stage progression outcomes (stage progression, RR = 0.47 (95% CI 0.28–0.78), p = 0.004, I2 = 67%); THA conversions, RR = 0.41 (95% CI 0.32–0.52), p < 0.001, I2 = 43%). Secondary outcomes (VAS, HHS, WOMAC score and Lequesne index) showed improved results when CD was combined with other regenerative agents, such as bone mesenchymal stem cells (BMSCs) and bone morphogenetic proteins (BMPs), etc. In the reported data, the regenerative group demonstrated significantly higher rates of subjective improvement in pain and functional outcomes compared to those in the CD group (71.74% (66/92) vs. 56.38% (53/94). Subgroup analysis revealed superior outcomes in the low-dose (less than 20 mL) BMAC group and patients aged under 40 years old in stage progression rate and THA conversion rate. Conclusion CD, when combined with regenerative therapy, can diminish hip pain and enhance functionality, but its ability to slow disease progression remains uncertain. BMAC presents a more substantiated efficacy evidence than other agents, with low-doses of BMAC in patients under 40 years potentially slowing ONFH progression. Nonetheless, the high heterogeneity and relatively short follow-up time of these studies make it difficult to draw accurate conclusions, which necessitates verification through future trials comparing CD versus CD combined with regenerative therapy, with a focus on extended follow-up periods. Systematic Review Registration identifier CRD42023467873.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.031
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.034
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.388
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations10
Published2025
Admission routes1
Has abstractyes

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