MétaCan
Menu
Back to cohort
Record W2116181995 · doi:10.1186/bcr1201

Imaging in breast cancer: Single-photon computed tomography and positron-emission tomography

2005· review· en· W2116181995 on OpenAlexaff
François Bénard, Éric Turcotte

Bibliographic record

VenueBreast Cancer Research · 2005
Typereview
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsBreast cancerPositron emission tomographyMedicineMammographySurgical oncologyRadiologyCancerComputed tomography laser mammographyMolecular imagingBreast imagingNuclear medicinePreclinical imagingOncologyInternal medicine

Abstract

fetched live from OpenAlex

Although mammography remains a key imaging method for the early detection and screening of breast cancer, the overall accuracy of this test remains low. Several radiopharmaceuticals have been proposed as adjunct imaging methods to characterize breast masses by single-photon-emission computed tomography (SPECT) and positron-emission tomography (PET). Useful in characterizing indeterminate palpable masses and in the detection of axillary metastases, these techniques are insufficiently sensitive to detect subcentimetric tumor deposits. Their role in staging nodal involvement of the axillary areas therefore currently remains limited. Several enzymes and receptors have been targeted for imaging breast cancers with PET. [18F]fluorodeoxyglucose is particularly useful in the detection and staging of recurrent breast cancer and in assessing the response to chemotherapy. Several other ligands targeting proliferative activity, protein synthesis, and hormone and cell-membrane receptors may complement this approach by providing unique information about biological characteristics of breast cancer across primary and metastatic tumor sites.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.068
GPT teacher head0.437
Teacher spread0.370 · 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.

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

Citations91
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueBreast Cancer ResearchSame topicMedical Imaging Techniques and ApplicationsFrench-language works237,207