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Record W3111264582 · doi:10.1186/s41824-021-00098-y

Diagnostic accuracy of SPECT/CT arthrography in patients with suspected aseptic joint prostheses loosening

2021· article· en· W3111264582 on OpenAlexaff
Bo Bao, Crystal S. Liu, Edward Masson, Jonathan Abele

Bibliographic record

VenueEuropean Journal of Hybrid Imaging · 2021
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsHealth Sciences CentreUniversity of CalgaryUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineArthrogramProsthesisPredictive valueRadiologyArthroplastyDiagnostic accuracyNuclear medicineSurgeryRadiographyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the diagnostic accuracy of SPECT/CT arthrography in patients with suspected aseptic prosthesis loosening following hip and knee arthroplasty. METHODS: A retrospective review of 63 SPECT/CT arthrogram studies (36 knees and 27 hips) between February 1, 2013, and July 1, 2018, was conducted. All patients underwent clinical and radiologic evaluation as part of their assessment for persistent pain following hip and knee arthroplasty. The detection of tracer activity along the bone-prosthetic interface on SPECT/CT suggests aseptic loosening. Operative assessment as well as clinical/radiologic follow-up at a minimum of 1 year was used as the reference standard. RESULTS: The sensitivity and specificity of SPECT/CT for detection of aseptic loosening was 6/7 (86%) and 55/56 (98%), respectively. This gives a positive predictive value (PPV) of 6/7 (86%), a negative predictive value (NPV) of 55/56 (98%), and a diagnostic accuracy of 61/63 (97%). CONCLUSION: SPECT/CT arthrography has a high diagnostic accuracy (97%) in the evaluation of loosening of both hip and knee arthroplasties in patients with persistent post-procedural pain.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2021
Admission routes1
Has abstractyes

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