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Record W2945046785 · doi:10.5858/arpa.2018-0463-ra

Needle Biopsy Adequacy in the Era of Precision Medicine and Value-Based Health Care

2019· review· en· W2945046785 on OpenAlexaff
Kenneth P. H. Pritzker, Heikki J. Nieminen

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2019
Typereview
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)MedicineBiopsyMedical physicsModalitiesHealth careRadiologyIntensive care medicine

Abstract

fetched live from OpenAlex

CONTEXT.—: Needle biopsy of diseased tissue is an essential diagnostic tool that is becoming even more important as precision medicine develops. However, the capability of this modality to efficiently provide samples adequate for diagnostic and prognostic analysis remains quite limited relative to current diagnostic needs. For physicians and patients, inadequate biopsy frequently leads to diagnostic delay, procedure duplication, or insufficient information about tumor biology leading to delay in treatment; for health systems, this results in substantial incremental costs and inefficient use of scarce specialized diagnostic resources. OBJECTIVE.—: To review current needle biopsy technology, devices, and practice with a perspective to identify current limitations and opportunities for improvement in the context of advancing precision medicine. DATA SOURCES.—: PubMed searches of fine-needle aspiration and core needle biopsy devices and similar technologies were made generally, by tissue site, and by adequacy as well as by health economics of these technologies. CONCLUSIONS.—: Needle biopsy adequacy can be improved by recognizing the importance of this diagnostic tool by promoting common criteria for needle biopsy adequacy; by optimizing needle biopsy procedural technique, technologies, clinical practice, professional education, and quality assurance; and by bundling biopsy procedure costs with downstream diagnostic modalities to provide better accountability and incentives to improve the diagnostic process.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.357
Teacher spread0.314 · 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 designOther design
Domainnot available
GenreReview

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

Citations61
Published2019
Admission routes1
Has abstractyes

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