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Record W2314054433 · doi:10.5858/arpa.2012-0223-le

Intersection of Surgical Pathology and Molecular Diagnostics for Targeted Therapy: Recommendation for Synoptic Reporting

2012· letter· en· W2314054433 on OpenAlexaboutno aff
Timothy C. Greiner

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2012
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgical pathologyMolecular pathologyMedical diagnosisCompanion diagnosticPathologyGeneral surgeryMedical physicsCancerInternal medicineBiology

Abstract

fetched live from OpenAlex

To the Editor.–The College of American Pathologists (CAP) has helped develop synoptic reports for cancers in different organ systems. These reports have been created to provide uniform data for tumor characteristics and staging to assist our clinical colleagues in treating their patients. In the past few years we have seen the development of targeted therapy for mutations or structural abnormalities in solid tumors. The molecular assays for these therapies often require the choice of a block with the highest tumor percentage in a biopsy, resection, or metastasis specimen.This task frequently requires going back to a case that may be several years old to choose a block, a process that takes time in a busy schedule making primary diagnoses. In the past, surgical pathology reports have not been written to accommodate this type of testing. The CAP held a companion symposium titled “Bridging the Divide Between Molecular and Surgical Pathology” at the United States and Canadian Association of Pathology meeting in Vancouver, British Columbia, Canada, in March 2012. At that symposium, I made the recommendation that it would be beneficial for synoptic reports to include an identification of the best block(s) with the highest percentage of tumor cells (recording the percentage). If the designation of 1 or more blocks is done in the synoptic report, it will save time for block selection by the surgical pathologist, the covering person when one is on vacation or has left the practice, or the molecular pathologist. The best time to record a good block is when all the sides of a case are evaluated the first time, along with any adjunctive immunohistochemical stains. Some pathologists may be concerned that such an action may obviate the Current Procedural Terminology code for archival selection of tissue material (88363). An informed decision still needs to be made, as the best choice of a block is dependent on the tumor type, the specific assay requirements, and the material available (biopsy, resection of primary tumor, or metastatic lesion). I think the synoptic report change will benefit everyone: surgical pathologists, molecular pathologists, and other laboratory professionals, and most of all, the patient; it will help get the best molecular answer in the shortest time possible.

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)0.003
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: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.305
Teacher spread0.281 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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