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Record W3009382725 · doi:10.1177/1071100720910422

Nuclear Medicine for the Orthopedic Foot and Ankle Surgeon

2020· review· en· W3009382725 on OpenAlexaff
Joseph Serino, Kyle N. Kunze, Stephen Jacobsen, Joel Morash, George B. Holmes, Johnny Lin, Simon Lee, Kamran S. Hamid, Daniel D. Bohl

Bibliographic record

VenueFoot & Ankle International · 2020
Typereview
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineAnkleOrthopedic surgeryPlantar fasciitisFoot (prosody)OsteomyelitisNuclear medicine imagingScintigraphyMagnetic resonance imagingRadiologyStress fracturesSports medicineBone scintigraphyFoot and ankle surgeryAnkle replacementNuclear medicineSurgeryHeelPhysical therapyAnatomy

Abstract

fetched live from OpenAlex

Nuclear medicine has been widely applied as a diagnostic tool for orthopedic foot and ankle pathology. Although its indications have diminished with improvements in and the availability of magnetic resonance imaging, nuclear medicine still has a significant and valuable role. The present article offers a comprehensive and current review of the most common nuclear imaging modalities for the orthopedic foot and ankle surgeon. Methods discussed include bone scintigraphy, gallium citrate scintigraphy, labeled-leukocyte scintigraphy, and single-photon emission computed tomography (SPECT). We review the indications and utility of these techniques as they pertain to specific foot and ankle conditions, including osteomyelitis, stress fractures, talar osteochondral lesions, complex regional pain syndrome, oncology, plantar fasciitis, and the painful total ankle arthroplasty. We conclude with a discussion of our approach to nuclear medicine with illustrative cases. Level of Evidence: Level V, expert opinion.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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