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Record W4405054702 · doi:10.1182/blood-2024-210641

Diagnostic Test Accuracy of Fat Pad Sampling and Bone Marrow Biopsies in the Diagnosis of AL Amyloidosis: A Systematic Review and Meta-Analysis

2024· review· en· W4405054702 on OpenAlexaff
Muayad Azzam, Hassan Kawtharany, Jamil Nazzal, Vishal Kukreti, Matthew D. Seftel, Aseel Alkhader, Qais Hamarsha, Tala Khraise, Mahmoud Al-Masri, Hadi Khaled Abou Zeid, Iktimal Alwan, Noor Jaber, Mustafa Qadir, Antonia Carroll, Angela Dispenzieri, Jack Khouri, Maria M. Picken, Shahzad Raza, Hira Shaikh, María Adela Aguirre, Deborah Boedicker, Naresh Bumma, Joselle Cook, Raymond L. Comenzo, Faizi Jamal, Noel R. Dasgupta, Alfredo H. De La Torre, Nelson Leung, Vaishali Sanchorawala, Nitasha Sarswat, Reem A. Mustafa

Bibliographic record

VenueBlood · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsQueen Elizabeth II Health Sciences CentrePrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineBiopsyAmyloidosisAL amyloidosisGold standard (test)Grading (engineering)PathologyRadiology

Abstract

fetched live from OpenAlex

Introduction: Tissue biopsies are needed to establish a diagnosis of systemic AL amyloidosis. Biopsy can be obtained from an involved organ with suspected amyloid deposition (target biopsy) or from sites in which amyloid commonly deposits (surrogate biopsy). The choice of biopsy site depends on institutional and physician preference. Two commonly used sites for surrogate biopsies are abdominal fat pad and bone marrow. The present systematic review and meta-analysis aims to evaluate the diagnostic test accuracy of fat pad and bone marrow biopsies as surrogate biopsy sites in the diagnosis of AL amyloidosis. Methods: As part of the ASH AL Amyloidosis Diagnostic Guideline, we performed systematic reviews for seven prioritized PICO questions related to the screening, diagnosis and organ involvement evaluation of AL amyloidosis. We conducted electronic searches on PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception until January 2024. Two reviewers independently and in duplicate performed title and abstract screening and full text article screening on LASER AI, with conflicts resolved by a third reviewer. Studies were eligible if they included patients that had a fat pad sample or bone marrow biopsy and a target organ biopsy for suspicion of AL amyloidosis allowing for the calculation of diagnostic test accuracy measures. Diagnostic test accuracy measures of interest were sensitivity, specificity, positive and negative predictive value. Statistical analysis was performed on OpenMeta[Analyst]. Grading of Recommendations Assessment, Development and Evaluation (GRADE) was used to assess the certainty of evidence. Results: After deduplication, we retrieved 29,237 studies.. 20 studies with 2955 patients reporting on bone marrow biopsy, 25 with 4702 patients on fat pad sampling were included. The pooled sensitivity for bone marrow biopsy was 55.1% (95% CI 45.8-64.0, I2 = 94%) (Certainty of evidence: Low ⨁⨁◯◯), For fat pad sampling sensitivity was 76.6% (95% CI 72.1-80.8, I2 = 84.2%) (Certainty of evidence: Moderate ⨁⨁⨁◯). Among the included studies, 1 evaluated the sensitivity of fat pad sampling and bone marrow biopsy combined and found it to be 89%. No included studies evaluated specificity for bone marrow biopsy or fat pad sample. Conclusion: Combination of fat pad and bone marrow biopsy together significantly improve sensitivity for amyloid detection compared to either fat pad sampling or bone marrow biopsy alone.

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)0.031
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.099
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.042
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.349
Teacher spread0.288 · 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 designMeta-analysis
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

Citations2
Published2024
Admission routes1
Has abstractyes

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