MétaCan
Menu
Back to cohort
Record W4392578791 · doi:10.29007/mtgn

NOFUSS: Navigated Orthopaedic Fixation using Ultrasound System

2024· article· en· W4392578791 on OpenAlexaff
Prashant Pandey, Emily K. Bliven, Pierre Guy, Kelly A. Lefaivre, Antony J. Hodgson

Bibliographic record

VenueEPiC series in health sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluoroscopyUltrasoundSacroiliac jointCadaverFixation (population genetics)MedicineComputer scienceBiomedical engineeringRadiologySurgery

Abstract

fetched live from OpenAlex

Fractures of the sacroiliac joint often require treatment through internal fixation. This procedure is typically guided by the use of intraoperative fluoroscopy, using an untracked C-arm device. However, this involves ionizing radiation exposure and the possibility of screw malplacement. We introduce the Navigated Orthopaedic Fixation using Ultrasound System (NOFUSS): an ultrasound (US) based end-to-end system for providing real-time navigation for iliosacral screw (ISS) insertions. Our system consists of an US imaging device and an optical tracking camera, together with computational algorithms for automatic processing of intraoperative data. In a cadaver trial of 6 specimens, we found that the ISS insertions performed using NOFUSS demonstrated accuracy comparable to conventional fluoroscopy guidance in the three specimens for which we could obtain good ultrasound images, reduced insertion time, and required no ionizing radiation.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.036
GPT teacher head0.360
Teacher spread0.324 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEPiC series in health sciencesSame topicPelvic and Acetabular InjuriesFrench-language works237,207