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Record W2127159164 · doi:10.1016/j.carj.2009.05.011

Is Magnetic Resonance Imaging a Suitable Imaging Modality for the Diagnosis of Osteomyelitis of the Foot or Ankle?

2009· review· en· W2127159164 on OpenAlexaff
Rohit Joshi, Stephen M. Smith

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2009
Typereview
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAnkleDiagnostic odds ratioOsteomyelitisRadiologyMagnetic resonance imagingRadiographyFoot (prosody)Bone biopsyModality (human–computer interaction)OsteitisOdds ratioNuclear medicineBiopsyDiagnostic accuracySurgeryPathology

Abstract

fetched live from OpenAlex

A systematic meta-analysis [1] found 16 studies from which 2 2 contingency tables could be constructed and used to extract information about foot and ankle cases suspected of osteomyelitis. The studies selected for review used extraction instruments derived from the Cochrane Methods Group checklist of Systematic Review of Screening and Diagnostic Tests [2]. These studies compared MRI with either plain radiography, technetium Tc99m bone scan, or white blood cell (WBC) scan. Of the studies included for review, at least 80% of the patients were 16 years of age and older. The diagnostic accuracy in each study was determined by using bone biopsy. Sensitivity and specificity were calculated for all articles, and summary receiver operator curves (ROC) were calculated for each imaging modality. The diagnostic odds ratio (DOR) describes the ratio of the

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.014
Bibliometrics0.0070.008
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.326
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2009
Admission routes1
Has abstractyes

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