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Record W2966294031 · doi:10.1259/bjr.20190506

Beyond the bones and joints: a review of ligamentous injuries of the foot and ankle on 99mTc-MDP-SPECT/CT

2019· review· en· W2966294031 on OpenAlexaff
Ramin Mandegaran, Sukhvinder Dhillon, Ho Jen

Bibliographic record

VenueBritish Journal of Radiology · 2019
Typereview
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnkleMedicineFoot (prosody)Soft tissueSingle-photon emission computed tomographyRadiologyNuclear medicineAnatomy

Abstract

fetched live from OpenAlex

99mTc-MDP-single photon emission computed tomography (SPECT)/CT has established itself as a useful modality in the assessment of the painful foot and ankle, owing to its ability to depict anatomical and functional information simultaneously. Despite its growing popularity, much of the literature describing the utility of 99mTc-MDP-SPECT/CT of the foot and ankle is limited to osseous and articular pathology, with descriptions of ligamentous pathology limited to just a handful of cases. Though assessment of soft tissues using a combination of bone tracer and CT is limited, with a sound understanding of the regional anatomy, it is certainly within the scope of 99mTc-MDP-SPECT/CT to accurately identify ligamentous injuries based on focal tracer uptake patterns and suspected ligamentous injuries should be reported as such. This article provides a case-based pictorial review of the 99mTc-MDP-SPECT/CT appearances of the various ligamentous injuries of the foot and ankle with case examples and MRI correlation where available, most of which have yet to be described. The typical patterns of bone tracer uptake and associated CT abnormalities that may be observed are 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)0.001
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 score0.960
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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