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Record W3035878334 · doi:10.2340/20030711-1000012

CD34+ stem cell treatment for knee osteoarthritis: a treatment and rehabilitation algorithm

2018· review· en· W3035878334 on OpenAlexaff
Anna Kubsik, Kamil Klupiński, Marek Krochmalski, Jakub Krochmalski, Paulina Klimkiewicz, Marta Woldańska-Okońska

Bibliographic record

VenueJournal of Rehabilitation Medicine – Clinical Communications · 2018
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsMagnus Chemicals (Canada)
Fundersnot available
KeywordsOsteoarthritisRehabilitationMedicineOrthopedic surgeryPhysical therapyKnee JointPathologicalPhysical medicine and rehabilitationAlgorithmSurgeryPathologyAlternative medicineComputer science

Abstract

fetched live from OpenAlex

Osteoarthritis is a group of multiple overlapping pathological conditions that cause destruction of articular cartilage and other structures of the joint. It is a progressive disease that leads to limitations of physical activity. New forms of treatment are therefore sought to alleviate the clinical symptoms of osteoarthritis and avoid surgery. Stem cell based therapy is an emerging field in orthopaedics. This study describes the treatment of knee osteoarthritis with CD34+ stem cells at the Medical Magnus Outpatient Clinic in Lodz, Poland, together with the treatment and rehabilitation algorithm developed for maximum effectiveness of this procedure. The algorithm includes 3 rehabilitation stages: preoperative, hospitalization and outpatient periods.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.216
GPT teacher head0.513
Teacher spread0.296 · 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 designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

Explore more

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