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Record W4296716944 · doi:10.1503/cjs.019221

Position statement: nonoperative management of lateral epicondylitis in adults

2022· review· en· W4296716944 on OpenAlexaffvenue
Peter Lapner, Ana Alfonso-Fernández, Jonah Herbert-Davies, JW Pollock, Jonathan Marsh, Graham J.W. King

Bibliographic record

VenueCanadian Journal of Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of ManitobaPan Am ClinicOttawa HospitalSt Joseph's Health CareUniversity of OttawaWestern University
Fundersnot available
KeywordsEpicondylitisMedicinePhysical therapyMEDLINECochrane LibraryGuidelinePlaceboOrthopedic surgeryTennis elbowEvidence-based medicineVisual analogue scaleGrading (engineering)Randomized controlled trialSurgeryAlternative medicineElbow

Abstract

fetched live from OpenAlex

We sought to compare methods of nonsurgical treatment of lateral epicondylitis in men and women older than 18 years to develop a guideline intended for orthopedic surgeons and other health care providers who assess, counsel and care for these patients. We searched Medline, Embase and Cochrane through to Mar. 9, 2021, and included all English-language studies comparing nonsurgical approaches. We compared physiotherapy versus no active treatment, corticosteroids versus placebo, platelet-rich plasma (PRP) versus placebo, and autologous blood injection versus placebo. Outcomes of interest were pain outcomes (visual analogue scale scores) and functional outcomes. We rated the quality of the evidence and strength of recommendations using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. This guideline will benefit patients seeking nonsurgical intervention for lateral epicondylitis by improving counselling on nonsurgical treatment options and possible outcomes. It will also benefit surgical providers by improving their knowledge of various nonsurgical approaches. Data presented could be used to develop frameworks and tools for shared decision-making.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.311
Teacher spread0.250 · 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.

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

Citations12
Published2022
Admission routes2
Has abstractyes

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