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Record W1964574624 · doi:10.5858/arpa.2013-0282-cp

Do Liquid-Based Preparations of Pulmonary Bronchial Brushing Specimens Perform Differently From Classically Prepared Cases for the Diagnosis of Malignancies? Observations From the College of American Pathologists Interlaboratory Comparison Program in Nongynecologic Cytology

2015· article· en· W1964574624 on OpenAlexaff
Z. Laura Tabatabai, Manon Auger, Daniel Kurtycz, Rodolfo Laucirica, Rhona J. Souers, Ritu Nayar, Walid E. Khalbuss, Ann Moriarty, Mostafa Fraig

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer and biochemical research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsConcordanceMedicinePapanicolaou stainMalignancyMedical diagnosisBasal cellCarcinomaPapanicolaou TestInternal medicinePathologyRadiologyGastroenterologyCancerCervical cancer

Abstract

fetched live from OpenAlex

CONTEXT: Pulmonary bronchial brushing specimens can be processed by liquid-based or conventional methods. The ability to accurately diagnose a pulmonary malignancy with a liquid-based preparation (LBP) versus a conventional preparation may differ. OBJECTIVE: To compare the performance of LBPs of malignant pulmonary bronchial brushing specimens with the performance of conventional preparations. DESIGN: Participant responses from 553 malignant pulmonary bronchial brushing samples were evaluated for concordance with the general diagnosis. The performance of LBPs was compared with that of classic preparations. A nonlinear mixed model was used to analyze the performance by reference diagnosis, preparation type, program years, participant type, and the interaction terms between these 4 factors. RESULTS: Concordance with the general category of malignant disease was observed in 95.2% of conventional Papanicolaou-stained, 90.9% of modified Giemsa-stained, and 96.9% of LBP (P < .001) samples. The results were significantly different between individual reference diagnoses (P < .001). The performance of LBPs was consistently higher for most diagnoses and was significantly better for squamous cell carcinoma (P = .005), small cell carcinoma (P < .001), and metastatic carcinoma not otherwise specified (P < .001). All participant types performed significantly better with LBPs of small cell carcinoma. Pathologists and cytotechnologists performed significantly better with LBPs of squamous cell carcinoma. A significantly higher concordance was observed between the general diagnosis and program years 2007-2011 versus 2001-2006 (P = .006). CONCLUSIONS: Liquid-based preparations performed better than conventional methods, with significantly higher performance in squamous cell, small cell, and metastatic carcinomas. Improved performance over time may reflect more frequent use of LBP methods and increased familiarity with interpreting the morphologic findings.

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)0.016
metaresearch head score (Gemma)0.058
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 score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.353
Teacher spread0.292 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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