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Record W4231340752 · doi:10.1179/014788805794775334

Issues About Tissues, Part I: The Objectives of Histopathology

2005· article· en· W4231340752 on OpenAlexaboutno aff
P J Bryant

Bibliographic record

VenueJournal of Histotechnology · 2005
Typearticle
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsnot available
Fundersnot available
KeywordsHistopathologyMedicinePathologyAutopsyBiopsyDemiseMedical physicsGeneral surgeryPolitical science

Abstract

fetched live from OpenAlex

This work was presented at the 30th National Society for Histotechnology Symposium at Toronto in 2004, where it described the importance and recognition of histopathology as a valuable diagnostic tool. Histopathology is a stimulating and demanding science, and it is the interpretation of biopsies and smears that remains one of the principal systems for predicting the biological behavior of disease and for controlling patient management. This work not only considers the diagnosis but describes, through the adoption of strict laboratory guidelines, how diagnostic fallibility can be minimized. Despite its demise, the value of the autopsy in clinicopathological correlation, biopsy technique, and education of anatomy also is described. (The J Histotechnol 28:63, 2005)Submitted December 7, 2004; accepted with revisions February 9, 2005

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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 designNot applicable
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

Citations0
Published2005
Admission routes1
Has abstractyes

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