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Validation of an alignment method using motion tracking system for <i>in-vitro</i> orientation of cadaveric hip joints with reduced set of anatomical landmarks

2017· article· en· W2775585232 on OpenAlexaff
Suzan Bsat, Ifaz T. Haider, Andrew Speirs, Paul E. Beaulé, Hanspeter Frei

Bibliographic record

VenueMedical Engineering & Physics · 2017
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsOttawa HospitalUniversity of OttawaCarleton University
Fundersnot available
KeywordsCadaveric spasmOrientation (vector space)PelvisFemurCadaverBiomedical engineeringComputer visionArtificial intelligenceComputer scienceOrthodonticsGeometryAnatomyMathematicsMedicineSurgery

Abstract

fetched live from OpenAlex

Accurate in-vitro orientation of cadaveric hip joints is challenging due to limited available anatomical landmarks. Published hip joint in-vitro investigations commonly lack details on methods used to achieve reported orientations and the accuracy with which the desired orientation has been achieved. The aim of this study was to develop an accurate method for orienting hip joints with limited anatomical landmarks for in-vitro investigations, and to compare this method against orientation using guiding axes and by visual approximation. The proposed orientation method resulted in orientation angles achieved to within one degree (SD ± 0.58°). For most specimens, orientation using physical tools resulted in errors of ±8° and ±12° in at least one of three orientation angles used to place the femur and pelvis in neutral orientation, respectively. Precision was also worse, with SDs ranging from ±1° to ±5° for orientation angles of femoral specimens and SDs ranging from ±1° to ±8° for pelvic specimens. The error in the orientation angles was worse for orientation by visual approximation and the range of SDs were greater for both the femur and pelvis. Finite element modeling was used to assess the effects of observed orientation errors, on prediction of fracture load. In most cases, the largest error in fracture load among all trials exceeded 30%, relative to a femur oriented without any error in the orientation angles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.303
Teacher spread0.279 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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