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Record W4376631217 · doi:10.3390/healthcare11091312

Meniscal Allograft Transplants in Skeletally Immature Patients: A Systematic Review of Indications and Outcomes

2023· review· en· W4376631217 on OpenAlexaboutno aff
Marco Turati, Linda Boerci, Massimiliano Piatti, Laura Russo, L Rigamonti, Francesco Buonanotte, Aurélien Courvoisier, Giovanni Zatti, Daniele Piscitelli, Marco Bigoni

Bibliographic record

VenueHealthcare · 2023
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryMeniscusComplicationOrthopedic surgeryIncidence (geometry)

Abstract

fetched live from OpenAlex

Meniscal lesions in skeletally immature patients can lead to joint degradation and knee instability. Meniscal allograft transplant (MAT) surgery is a solution to maintain knee stability. There is a lack of consensus on MAT surgery outcomes in pediatric patients. A systematic review was conducted according to the PRISMA guidelines. PubMed, Scopus and EMBASE databases were searched from 1965 to June 2022. Studies were evaluated using the Newcastle-Ottawa Scale (NOS). Three studies were selected, and 58 patients were included (mean age 15.9 years) in total. The lateral meniscus was involved in 82.8% of all MAT surgeries. Post-meniscectomy syndrome and discoid meniscus were the main indications for MAT surgery. All studies reported improved subjective clinical scores and levels of sport after the surgery. The complication rate was 27.5%. Partial meniscectomy, meniscus knot removal, chondral defect treatment and lysis of adhesions were the most frequent procedures performed during reoperation. MAT surgery can improve clinical outcomes in pediatric patients with strictly selected indications. MAT surgery is safe when there are no limb asymmetries or malalignments, but it remains a challenging procedure with a high complication rate. Long-term follow-up is needed for definitive statements on the use of MAT in skeletally immature patients.

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 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 score0.032
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.388
Teacher spread0.359 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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