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Record W2400690962 · doi:10.1002/cncy.21739

Diagnostic concordance of non–small cell lung carcinoma subtypes between biopsy and cytology specimens obtained during the same procedure

2016· article· en· W2400690962 on OpenAlexaffabout
Mojgan Ebrahimi, Manon Auger, Sungmi Jung, Richard S. Fraser

Bibliographic record

VenueCancer Cytopathology · 2016
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsConcordanceSubtypingMedicineBiopsyCytologyLung cancerNot Otherwise SpecifiedPathologyCancerRadiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The objectives of this study were: 1) to determine the diagnostic concordance of non-small cell lung carcinoma (NSCLC) subtypes in cytology and biopsy specimens taken during the same procedure and evaluate the causes of discordance; and 2) to determine the frequency of immunohistochemistry (IHC) use for subtyping NSCLC. METHODS: Biopsy and cytology specimens that were obtained at the same procedure and diagnosed as NSCLC between January 2011 and December 2014 at the McGill University Health Center were identified (n = 226 pairs). The diagnostic concordance between the 2 methods was evaluated. The slides from discordant cases were reviewed, and final diagnoses were made based on IHC, resection specimens, or pathologist discussion. RESULTS: Concordance in subtype diagnosis was perfect (adeno-adeno or squamous-squamous) in 66.2% of cases and was partial (adeno or squamous vs non-small cell) in 23%; discordance (adeno vs squamous) was observed in 7.8%. Although subtyping was not possible (ie, the final diagnosis was NSCLC, not otherwise specified) in 12.8% of biopsy specimens and 16.3% of cytology specimens, specific subtyping was not achieved in only 3% of cases when both modalities were considered. IHC was used in 47% of biopsy cases and 13% of cytology cases. CONCLUSIONS: Subtyping of NSCLC can be achieved in most cases (97%) by considering findings in both biopsy and cytology specimens, and concordance in subtyping between cytology and biopsy specimens can be reached in a high percentage of cases (89.2%). Cancer Cytopathol 2016;124:737-43. © 2016 American Cancer Society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.260
Teacher spread0.250 · 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 teacher head, 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

Citations14
Published2016
Admission routes2
Has abstractyes

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