MétaCan
Menu
Back to cohort
Record W2097740387 · doi:10.1155/2013/232968

Can Axial-Based Nodal Size Criteria Be Used in Other Imaging Planes to Accurately Determine “Enlarged” Head and Neck Lymph Nodes?

2013· article· en· W2097740387 on OpenAlexaff
Eric Bartlett, Thomas D. Walters, Eugene Yu

Bibliographic record

VenueISRN Otolaryngology · 2013
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsCoronal planeSagittal planeNuclear medicineMedicineGeometryAlgorithmMaterials scienceMathematicsAnatomy

Abstract

fetched live from OpenAlex

Objective. We evaluate if axial-based lymph node size criteria can be applied to coronal and sagittal planes. Methods. Fifty pretreatment computed tomographic (CT) neck exams were evaluated in patients with head and neck squamous cell carcinoma (SCCa) and neck lymphadenopathy. Axial-based size criteria were applied to all 3 imaging planes, measured, and classified as "enlarged" if equal to or exceeding size criteria. Results. 222 lymph nodes were "enlarged" in one imaging plane; however, 53.2% (118/222) of these were "enlarged" in all 3 planes. Classification concordance between axial versus coronal/sagittal planes was poor (kappa = -0.09 and -0.07, resp., P < 0.05). The McNemar test showed systematic misclassification when comparing axial versus coronal (P < 0.001) and axial versus sagittal (P < 0.001) planes. Conclusion. Classification of "enlarged" lymph nodes differs between axial versus coronal/sagittal imaging planes when axial-based nodal size criteria are applied independently to all three imaging planes, and exclusively used without other morphologic nodal data.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

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)0.0010.000

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 head0.036
GPT teacher head0.324
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 teacher head, not a consensus.

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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueISRN OtolaryngologySame topicHead and Neck Cancer StudiesFrench-language works237,207