MétaCan
Menu
Back to cohort
Record W2059521532 · doi:10.1097/cnq.0000000000000056

Osteomyelitis

2015· review· en· W2059521532 on OpenAlexaff
Angela Macci Bires, B Kerr, Lynn George

Bibliographic record

VenueCritical Care Nursing Quarterly · 2015
Typereview
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsKerr Wood Leidal Associates (Canada)
Fundersnot available
KeywordsMedicineOsteomyelitisMagnetic resonance imagingPositron emission tomographyModalitiesRadiologyBone scintigraphyScintigraphyDiseaseIntensive care medicineMedical physicsSurgeryPathology

Abstract

fetched live from OpenAlex

Diagnosis, early intervention, and treatment of patients who have an infection are the basic foundations of patient care. Early, appropriate interventions are associated with decreased patient morbidity and mortality. Diagnostic procedures with clinical information and laboratory results are integral in the assessment of inflammatory diseases and the prevention of sepsis. Some of the imaging modalities currently used for the assessment of inflammation include computed tomography, plain radiography, positron emission tomography, technetium Tc 99m bone scintigraphy, magnetic resonance imaging, and leukocyte scintigraphy. In the case of patients who exhibit signs of osteomyelitis, it is necessary to understand that acute and chronic conditions are not based on the duration of the disease but on the histopathologic features of the disease. Although several imaging modalities are considered appropriate, there is not one singular procedure that is considered ideal. Rather, it is a combination of procedures and various other clinical factors. This article addresses some of the advantages and disadvantages of the modalities, with a focus on molecular imaging and the assessment of osteomyelitis.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.086
GPT teacher head0.460
Teacher spread0.373 · 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 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

Citations15
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCritical Care Nursing QuarterlySame topicOrthopedic Infections and TreatmentsFrench-language works237,207