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Record W3125169412

Appraisal and summary of patellofemoral pain clinical practice guideline.

2020· article· en· W3125169412 on OpenAlexaffabout
Gaelan Connell, Daphne To, Mariam Ashraf, Leslie Verville

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsCentre for Disability Prevention and RehabilitationOntario Tech UniversityPublic Health OntarioCanadian Memorial Chiropractic College
Fundersnot available
KeywordsGuidelineChiropracticMedicineClinical PracticeManual therapyPatellofemoral pain syndromePhysical therapyFamily medicineAlternative medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this commentary was to critically appraise the patellofemoral pain clinical practice guideline published by the Academy of Orthopaedic Physical Therapy in 2019 and to summarize their recommendations for chiropractic practice. METHODS: Quality and reporting of this guideline was assessed with the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument. Three reviewers independently scored between 1-7 (strongly disagree-strongly agree) for 23 items organized into six quality domains. RESULTS: AGREE II quality domain scores ranged between 57%-98%, with overall quality of the recommendation rated 89%. The guideline contained evidence summaries and/or recommendations for three topics: impairment/function-based diagnosis; examination; and interventions. CONCLUSION: Based on its methodological quality, we recommend the use of this guideline for the examination, diagnosis, and management of patellofemoral pain in chiropractic practice. A summary of recommendations from this guideline is presented for use within the scope of chiropractic practice in Canada.

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)0.050
metaresearch head score (Gemma)0.256
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 score0.075
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.256
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0150.012
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0050.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0110.006

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 head0.058
GPT teacher head0.287
Teacher spread0.229 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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