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Record W2914328041 · doi:10.1097/jsm.0000000000000715

Donor-Specific Human Leukocyte Antigen Antibody Formation After Distal Tibia Allograft and Subsequent Graft Resorption

2019· article· en· W2914328041 on OpenAlexaff
Christopher R. Liwski, Daryl Dillman, Robert Liwski, Ivan Wong

Bibliographic record

VenueClinical Journal of Sport Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineResorptionHuman leukocyte antigenAntibodyAntigenPathologyImmunology

Abstract

fetched live from OpenAlex

The association between donor-specific human leukocyte antigen (HLA) antibody formation and small bone allograft resorption has not been studied. We present the case of a patient treated for glenoid bone loss using a distal tibial allograft with Bankart repair who formed donor-specific HLA antibodies against the allograft and had subsequent graft resorption. X-ray and computed tomography (CT) scans were performed before and after surgery at standard checkpoints. Patient blood and serum samples were collected before and after surgery for HLA typing and HLA antibody testing. Human leukocyte antigen antibodies against the donor-specific HLA-A2 antigens were identified 6 weeks after surgery and were still detected at 5 months after surgery. At 6 months after surgery, a CT arthrogram revealed significant graft resorption. This case shows a temporal correlation between HLA antibody formation and clinical findings, potentially suggesting an association between HLA antibody formation and graft resorption. Further study is required to confirm this.

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.354
Teacher spread0.322 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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