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Record W2585285068 · doi:10.1302/2058-5241.2.160004

Platelet-rich plasma (PRP) for knee disorders

2017· review· en· W2585285068 on OpenAlexaff
Mohammad Shahid, Rik Kundra

Bibliographic record

VenueEFORT Open Reviews · 2017
Typereview
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsPlatelet-rich plasmaArticular cartilageMedicinePlateletPlasmapheresisOsteoarthritisPlatelet lysateCoagulationAutologous bloodSynovial fluidSurgeryInternal medicinePathologyImmunologyAntibody

Abstract

fetched live from OpenAlex

Abstract Platelet-rich plasma (PRP) is an autologous blood product with platelet concentrations above baseline values. The process involves the extraction of blood from the patient which is then centrifuged to obtain a concentrated suspension of platelets by plasmapheresis. It then undergoes a two-stage centrifugation process to separate the solid and liquid components of the anticoagulated blood. PRP owes its therapeutic use to the growth factors released by the platelets which are claimed to possess multiple regenerative properties. In the knee, PRP has been used in patients with articular cartilage pathology, ligamentous and meniscal injuries. There is a growing body of evidence to support its use in selected indications and this review looks at the most recent evidence. We also look at the current UK National Institute of Health & Clinical Excellence (NICE) guidelines with respect to osteoarthritis and the use of PRP in the knee. Cite this article: EFORT Open Rev 2017;2:28–34. DOI: 10.1302/2058-5241.2.160004.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.004

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 head0.293
GPT teacher head0.488
Teacher spread0.195 · 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 designSystematic review
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

Citations68
Published2017
Admission routes1
Has abstractyes

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