MétaCan
Menu
Back to cohort
Record W4230217047 · doi:10.1109/iembs.2007.4353118

Evaluation of Obstetric Gestures: An Approach Based on the Curvature of 3-D Positions

2007· article· en· W4230217047 on OpenAlexaff
Richard Moreau, V. Ochoa, Minh Tu Pham, Pierre Boulanger, T. Redarce, Olivier Dupuis

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGesturePath (computing)Computer scienceSet (abstract data type)KinematicsIndependence (probability theory)CurvatureTrajectorySimulationArtificial intelligenceHuman–computer interactionMathematicsStatisticsGeometry

Abstract

fetched live from OpenAlex

This paper presents a method to evaluate a gesture carried out by a resident obstetrician doctors by comparing it to a gesture carried out by an expert obstetrician doctors. The studied gesture is the forceps blade placement. Residents were recorded on a childbirth simulator while placing forceps blades. Their paths were compared in order to evaluate how similar they are to a reference path defined by an expert. The comparison method is developed with respect to expert requests: time independence and in considering the whole set of data and not only particular points. In order to respect these requests, the developed method lies on the correlation coefficient between the path curvatures. Residents have been trained on a simulator and their gestures were evaluated by comparing their path curvatures to reference path curvatures. Quantitative results confirm the qualitative analysis, residents become more similar to the reference while training on simulator.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.064
GPT teacher head0.275
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations8
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueConference proceedingsSame topicHuman Motion and AnimationFrench-language works237,207