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Record W4404122373 · doi:10.1177/03000605241291735

Effect of repeated intra-articular administration of collagen as a treatment for knee osteoarthritis: a case report

2024· article· en· W4404122373 on OpenAlexaboutno aff
Yong In, Saad Mohammed AlShammari, Man Soo Kim

Bibliographic record

VenueJournal of International Medical Research · 2024
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsMedicineOsteoarthritisVisual analogue scaleHyaluronic acidKnee painSurgeryIntra articularArticular cartilageAnesthesiaPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Intra-articular injections aimed at correcting underlying pathophysiological processes and providing pain relief are essential for managing knee osteoarthritis (KOA). Collagen, the primary component of articular cartilage, has a long half-life, making it a promising candidate for intra-articular injections with a low risk of serious side effects. The first of the two cases in this report involved a woman in her early 70s with a 6-year history of persistent left knee pain. The second case involved a woman in her mid-50 s with a >7-year history of right knee pain. Both patients received hyaluronic acid injections every 6 months, totaling 10 injections over 5 years. They were diagnosed with Kellgren-Lawrence grade 2 KOA. The patients received two 3-mL intra-articular injections of 6% collagen, administered at baseline and 6 months later. Improvements in clinical outcomes, including visual analog scale scores and Western Ontario and McMaster Universities Osteoarthritis Index scores, were observed and maintained in both patients, with no side effects reported. In summary, when collagen injections were administered to these two patients with KOA, clinical improvements lasted approximately 6 months, and repeated treatments demonstrated efficacy and safety similar to the initial course.

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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.034
GPT teacher head0.436
Teacher spread0.402 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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