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Record W2022657968 · doi:10.1586/era.10.11

Fusion imaging for parathyroid localization in primary hyperparathyroidism

2010· review· en· W2022657968 on OpenAlexaff
Daniel S. Levine, Sam M. Wiseman

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2010
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicinePrimary hyperparathyroidismRadiologyAdenomaParathyroid adenomaImage fusionModality (human–computer interaction)Computed tomographicComputed tomographySurgeryFusionPathologyArtificial intelligence

Abstract

fetched live from OpenAlex

Primary hyperparathyroidism, most commonly due to a solitary parathyroid adenoma, has become an increasingly diagnosed condition and is one with great potential for cure following appropriate surgical intervention. Functional imaging with Tc-99m-based radiopharmaceuticals has contributed significantly to increased success in preoperative adenoma localization but the advent of fusion of this functional information with anatomical details obtained with computed tomographic imaging has yielded a potentially more powerful clinical tool and one that is particularly well suited to ectopic adenomas and cases of prior failed surgery. Aspects of this modality are discussed with reference to software- and hardware-based fusion techniques and evidence for efficacy. The allied technique of PET/computed tomography is also discussed.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.403
Teacher spread0.367 · 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 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

Citations12
Published2010
Admission routes1
Has abstractyes

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