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Record W2028090328 · doi:10.1016/j.otohns.2007.02.020

Utility of Head and Neck Biopsies in the Evaluation of Posttransplant Lymphoproliferative Disorder

2007· article· en· W2028090328 on OpenAlexaff
Paolo Campisi, Upton Allen, Bo‐Yee Ngan, Michael Hawkes, Vito Forte

Bibliographic record

VenueOtolaryngology · 2007
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSerostatusMedicineBiopsyLymphoproliferative disordersOdds ratioTransplantationOrgan transplantationHead and neckConfidence intervalLymphomaInternal medicineSurgeryHuman immunodeficiency virus (HIV)ImmunologyViral load

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the diagnostic yield of head and neck biopsies in the evaluation of PTLD in children and to explore whether this yield was related to pretransplant Epstein-Barr virus (EBV) serostatus. STUDY DESIGN: This is a retrospective study of pediatric, post-solid-organ transplant recipients who have undergone a biopsy in the head and neck region to establish a diagnosis of PTLD. RESULTS: Fifty-six biopsies were performed in 46 patients four to 120 months after solid-organ transplantation. Biopsies yielded PTLD in 39.1% of patients. The odds of developing PTLD if a patient was seropositive for EBV at the time of transplantation was 0.26 (95% confidence interval, 0.064-1.056; P = 0.054). CONCLUSIONS: The high diagnostic yield of PTLD suggests that biopsies should be performed if PTLD is suspected in pediatric posttransplant patients. The results demonstrate a trend toward lower risk of PTLD among patients with pretransplant exposure to EBV.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.326
Teacher spread0.297 · 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

Citations10
Published2007
Admission routes1
Has abstractyes

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