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Record W4391873649 · doi:10.1093/jcag/gwad061.096

A96 A QUALITY ASSESSMENT STUDY TO DETERMINE IF TISSUE ACQUISTION AND SPECIMEN HANDLING IMPACT THE DIAGNOSTIC YIELD OF ENDOSCOPIC ULTRASOUND-GUIDED FINE NEEDLE ASPIRATION OF SOLID MASS

2024· article· en· W4391873649 on OpenAlexaff
Shahid Khan, Pamela Mathura, L Puttangunta, S. Girgis, J Zhang, Jan Nilsson, S Wesilenko

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEndoscopic ultrasoundYield (engineering)UltrasoundFine-needle aspirationMedicineQuality (philosophy)RadiologyMaterials scienceBiopsyPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract Background Endoscopic ultrasound (EUS)-guided fine needle aspiration biopsy (FNAB) of solid mass lesions has a sensitivity and specificity between 50-100%. Tissue acquisition and specimen handling are factors that may contribute to this variability. At our institution, 3 endoscopists perform EUS. The aspirated material obtained is expressed onto slides for cytology (prepared by a nurse) and solid tissue fragments transferred into formalin. At the discretion of the endoscopist, aspirated material is collected in saline for cell block preparation. Aims To assess the impact of tissue collection and specimen handling on diagnostic yield of EUS-FNAB of solid mass lesions. Methods A chart audit was completed for all patients undergoing EUS-FNAB of solid mass lesions between January 1, 2022 and December 31, 2022. Descriptive statistics were completed. A definite diagnosis was considered when calculating the diagnostic yield. Results A total of 184 patients (100 M, 84 F), mean age 64±13 years (range 14-89 years), underwent 200 EUS-FNABs by 3 endoscopists. Pancreatic masses were the most common indication in 118/200 (59%) cases. A 22-gauge FNAB needle was used in 189/200 (95%) cases. A total of 285 needle passes were performed in 149 cases (mean 1.9/case). In the remaining 51 cases (26%), the number of needle passes was not specified. Tissue samples were transported in formalin in 190 cases, on cytology slides in 170 cases, and in saline for cell block preparation in 41 cases. Overall, a definite diagnosis was achieved in 149/200 cases (75%). Stratifying for needle passes, a definite diagnosis was achieved in 22/39 (56%), 71/85 (84%), 20/24 (83%), and 1/1 (100%) cases that had 1, 2, 3, and 4 needle passes. Of the 51 cases with unspecified needle passes, a definite diagnosis was achieved in 35 cases (69%). The diagnostic yield obtained with saline for cell block was similar to that obtained with formalin and cytology slides (30/41 [73%] vs. 144/190 [76%] and 132/170 [78%]). Conclusions Increasing the minimum number of needles passes for tissue acquisition to 3 per case may increase the diagnostic yield of EUS-FNAB. Documenting the number of needles passes in the endoscopy report is an important quality indicator. Cytology slides and tissue in formalin should be considered standard of care but aspirated material should continue to be used for cell block preparation. However, there is some concern that saline as a transport medium may de-vitalize the aspirated material, so it should be replaced with formalin to preserve tissue integrity. Funding Agencies None

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)0.018
metaresearch head score (Gemma)0.055
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 score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.345
Teacher spread0.324 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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