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Record W2785484495 · doi:10.1177/0363546517751690

Preoperative Tibial Subchondral Bone Marrow Lesion Patterns and Associations With Outcomes After Isolated Meniscus Allograft Transplantation

2018· article· en· W2785484495 on OpenAlexaboutno aff
Bryan M. Saltzman, Eric J. Cotter, Jeffrey P. Stephens, Gregory L. Cvetanovich, Brett Madden, Kevin Wang, Adam B. Yanke, Brian J. Cole

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGrading (engineering)LesionMagnetic resonance imagingTransplantationSubchondral boneTibiaCoronal planeSurgeryMeniscusRadiologyNuclear medicineOsteoarthritisPathologyArticular cartilage

Abstract

fetched live from OpenAlex

BACKGROUND: The association between preoperative tibial subchondral bone marrow lesion (BML) patterns and outcomes after isolated meniscus allograft transplantation (MAT) are unknown. PURPOSE: To determine (1) if a superior classification means exists (ie, high interrater reliability [IRR]) for grading tibial subchondral BML before isolated MAT and (2) whether quality and/or severity of preoperative tibial subchondral BML patterns was associated with clinical outcomes and/or failure rates after isolated MAT. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: All patients who underwent isolated MAT with a single surgeon between October 2006 and February 2017 were identified. Three means were evaluated to quantify the degree of subchondral BML in the affected tibial-sided compartment: Welsch et al, based on maximum diameter of the lesion; Costa-Paz et al, based on appearance and location of the lesion; and Filardo et al, based on severity of findings. IRR was generated and compared among the 3 classifications. The preoperative magnetic resonance imaging (MRI) subchondral BML grading scheme with the highest IRR was then used to assess for associations with postoperative outcomes for those patients with >2-year follow-up, per a Spearman correlation matrix with each reviewer's grades. RESULTS: In total, 60 MRI scans were available for subchondral BML grading. Grader 1 identified the presence of subchondral BML in the tibia of the affected compartment in 40 (66.7%) of the available MRI scans, as compared with 38 (63.3%) for grader 2. The calculated IRRs with the Welsch et al and Costa-Paz et al classifications were rated "strong/almost perfect" agreement. A significant correlation was demonstrated between grader 1 with the Welsch et al grading scheme and outcome measures of KOOS pain (Knee injury and Osteoarthritis Outcome Score; negative correlation, P = .05), WOMAC pain (Western Ontario and McMaster Universities Osteoarthritis Index; positive correlation, P = .026), and Marx Activity Rating Scale (negative correlation, P = .019). A significant correlation was demonstrated between grader 2 with the Costa-Paz et al grading scheme and postoperative satisfaction (positive correlation, P = .018). There were no significant differences in survivorship based on gradings. CONCLUSION: Nearly two-thirds of patients who undergo isolated MAT have subchondral BML on preoperative MRI. Our findings suggest that increasing BML size (Welsch et al) is correlated with worse postoperative pain measures (KOOS pain, WOMAC pain) and worse activity ratings (Marx Activity Rating Scale). Additionally, increasing disruption or depression of the normal contour of the cortical surface, with or without lesion contiguity with the subjacent articular surface (Costa-Paz et al), is correlated with greater postoperative satisfaction.

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)0.009
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.007
GPT teacher head0.270
Teacher spread0.262 · 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 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

Citations28
Published2018
Admission routes1
Has abstractyes

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