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Record W1827739683 · doi:10.1186/2050-5736-3-s1-o48

Magnetic resonance guided focused ultrasound for noninvasive pain therapy of osteoid osteoma in children

2015· article· en· W1827739683 on OpenAlexafffund
Adam C. Waspe, Yuexi Huang, Ruby Endre, João Amaral, Joost de Ruiter, Fiona Campbell, Charles Mougenot, Kullervo Hynynen, Gregory J. Czarnota, James M. Drake, Michael Temple

Bibliographic record

VenueJournal of Therapeutic Ultrasound · 2015
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePhilips (Canada)Hospital for Sick Children
FundersHospital for Sick ChildrenFocused Ultrasound Foundation
KeywordsOsteoid osteomaMedicineUltrasoundMagnetic resonance imagingRadiologyFocused ultrasound

Abstract

fetched live from OpenAlex

Osteoid osteoma (OO), a small painful benign bone tumor, is the most common bone tumor in children. Pain is managed with nonsteroidal anti-inflammatory drugs but minimally invasive techniques, such as CT-guided laser ablation, have become a standard intervention. However, the potential for non-target injury is a concern as tissue temperature cannot be measured with CT and the laser induces temperatures >90°C for 10 minutes. It also includes risks from exposure to ionizing radiation, fracture, infection and transmitted thermal damage from the access needle. Magnetic resonance guided high intensity focused ultrasound (MRgHIFU) has been used successfully in small cohorts of adults with OO. The noninvasive nature of the energy means that procedures do not need to be conducted in a sterile environment since there is no mechanical penetration of the bone, reducing the chance of pathologic fracture and infection.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.056
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.039
GPT teacher head0.293
Teacher spread0.254 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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