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Record W4417201174 · doi:10.4103/ijpn.ijpn_43_25

Autologous Fat-derived Stem Cell Therapy in Osteoarthritis: A Case Report on Stromal Vascular Fraction and Nanofat Injection

2025· article· en· W4417201174 on OpenAlexaboutno aff
Karthic Babu Natarajan, Kiruthika Balakrishnan, Preya Rengaraj, Lakshmi Saipriya Yella

Bibliographic record

VenueIndian Journal of Pain · 2025
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsnot available
Fundersnot available
KeywordsStromal vascular fractionOsteoarthritisHyaluronic acidAdipose tissueStromal cellStem cellMesenchymal stem cellCell therapy

Abstract

fetched live from OpenAlex

Knee osteoarthritis (KOA) is a progressive joint disease that causes pain, stiffness, and functional decline. Many patients look for nonsurgical options due to patient- and surgery-related factors. The regenerative potential of stromal vascular fraction (SVF) is drawing attention in recent times. This case report presents a patient with bilateral Grade 3 KOA, undergoing SVF therapy due to failed conservative management and unwillingness for surgery. Adipose tissue was harvested through liposuction, mechanically emulsified, and centrifuged to isolate SVF, which in combination with nanofat and hyaluronic acid was injected into both knees under fluoroscopic guidance. There was a significant improvement in terms of pain and function, which was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index and Visual Analog Scale, at 1, 3, and 6 months posttreatment. This case highlights SVF as a viable, minimally invasive alternative for managing knee OA, with potential to delay disease progression.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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