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Record W2032152347 · doi:10.1155/2000/461695

Specific Changes of Chromatin Structure in Nuclei of Normal Epithelium Adjacent to Laryngeal Squamous Cell Carcinoma

2000· article· en· W2032152347 on OpenAlexaff
Thomas Dreyer, Iris Knoblauch, David M. Garner, Alexei Doudkine, Calum MacAulay, Branko Palcic, Christian Popella

Bibliographic record

VenueAnalytical Cellular Pathology · 2000
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsEpitheliumPathologyTexture (cosmology)MedicineChemistryBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this study was to confirm the existence of specific nuclear texture feature alterations of histologically normal epithelial borders nearby invasive laryngeal cancer (NC). Paraffin sections of NC and of chronic inflammations unrelated to cancer (CI) were analysed for nuclear texture and for integrated optical density (IOD-index) and were compared to normal epithelium of patients without evidence of cancer (NE). Several discriminant functions based on nuclear texture features were trained to separate different subgroups. As the most important result, specific nuclear texture feature shifts were only found in NC with high-density lymphocytic stroma infiltrate (NC+). Classification of nuclei of NE versus NC+ was correct in 70%. The same classifier was correct in only 58% when nuclei of NE were classified versus CI. We also found lower values of IOD-Index within the NC+ group when compared to NE (p < 0.001).

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.241
Teacher spread0.233 · 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

Citations6
Published2000
Admission routes1
Has abstractyes

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