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Systematic Review of Patient Outcomes and Associated Predictors After Microfracture in the Patellofemoral Joint

2019· review· en· W2984939328 on OpenAlexaff
Jason B. Smoak, Melissa A. Kluczynski, Leslie J. Bisson, John M. Marzo

Bibliographic record

VenueJAAOS Global Research and Reviews · 2019
Typereview
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicinePatellofemoral jointCINAHLOsteoarthritisSurgeryPhysical therapyPatellaPathology

Abstract

fetched live from OpenAlex

We summarized the clinical outcomes and predictors of clinical outcomes after microfracture for chondral lesions in the patellofemoral joint (PFJ). METHODS: Embase, PubMed, CENTRAL, BIOSIS, and CINAHL databases were searched between January 1, 1980, and January 1, 2019, to identify all articles that examined outcomes or predictors of outcomes of microfracture in patients with patellofemoral chondral lesions. Studies of full-thickness chondral lesions in the PFJ were included, whereas those involving adolescents, partial-thickness chondral lesions, and underlying patellar instability were excluded. RESULTS: We found a total of 257 articles, of which 8 articles (174 patients) met our inclusion criteria. All studies found improvement in clinical outcomes after microfracture in the PFJ. Younger patients showed greater improvement in clinical outcomes than older patients. However, the effect of size, severity (grade), or location of chondral lesions on clinical outcomes after microfracture is unclear. CONCLUSION: We found improvement in clinical outcomes after microfracture in the PFJ at midterm follow-up. Age may be a predictor of successful outcomes and longevity of the repair; however, there is insufficient evidence regarding the influence of defect size, severity, and location on clinical outcomes.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.353
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.363
Teacher spread0.276 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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