MétaCan
Menu
Back to cohort
Record W2164529218 · doi:10.1258/shorts.2011.011148

The role of platelet rich plasma in musculoskeletal science

2012· article· en· W2164529218 on OpenAlexaff
Zafar Ahmad, Daniel J. Howard, Roger A. Brooks, John Wardale, Frances Henson, Alan Getgood, Neil Rushton

Bibliographic record

VenueJRSM Short Reports · 2012
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsFowler Kennedy Sport Medicine ClinicWestern University
Fundersnot available
KeywordsPlatelet-rich plasmaMedicineAthletesMusculoskeletal painSports medicineSports sciencePlateletPhysical therapyMedical educationInternal medicinePhysiology

Abstract

fetched live from OpenAlex

The idea of using platelet rich plasma (PRP) in medicine has been around since the 1970s. It is only more recently that its use has been employed in the area of musculoskeletal science. Platelet rich plasma in this area has received much media attention being used by many celebrity sports athletes for musculoskeletal injuries. Therefore it is important for the musculoskeletal practitioner to be aware of the concepts surrounding its use and application. In this article we cover what platelet rich plasma is, how it is prepared and administered, its potential clinical application, and what the current literature discusses in the various areas of musculoskeletal science.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.315
Teacher spread0.302 · 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 designObservational
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

Citations102
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJRSM Short ReportsSame topicShoulder Injury and TreatmentFrench-language works237,207