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Record W4416455290 · doi:10.12968/hmed.2024.0959

Novel Molecular Imaging Approaches: Towards a Better Estimation of Response in Breast Cancer

2025· review· en· W4416455290 on OpenAlexaff
Amy R. Sharkey, Gary Cook

Bibliographic record

VenueBritish Journal of Hospital Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsBreast cancerMolecular imagingPositron emission tomographyBreast tumoursCancer imagingHuman breastBreast imagingPet imaging

Abstract

fetched live from OpenAlex

The use of [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography ([ 18 F]FDG PET/CT) in breast cancer response assessment and monitoring is well established. However, there are limitations not only to the use of [ 18 F]FDG PET/CT in breast cancer, but also deficiencies in the conventional imaging assessment of treatment response. Breast cancer is biologically heterogeneous, and heterogeneity of tumours limits the accuracy of [ 18 F]FDG PET/CT assessment in some subtypes of breast cancer. Increased understanding of tumour biology and the tumour microenvironment have led to the development of new, specific radio-tracers. These targeted tracers may offer a solution in terms of more accurate response assessment, and prognostication.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.029
GPT teacher head0.351
Teacher spread0.323 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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