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Record W2070973960 · doi:10.1109/tbme.2006.886619

Modelling Skin Pelvic Landmark Coordinates Into Corresponding Internal Bone for Wheelchair Users

2006· article· en· W2070973960 on OpenAlexaff
Nadine-Michle Lalonde, Jean Dansereau, Michle Lacoste, Rachid Aïssaoui

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2006
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier Universitaire Sainte-JustinePolytechnique Montréal
Fundersnot available
KeywordsLandmarkWheelchairComputer scienceComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the relationships, by linear regression, between internal and external pelvic landmarks identified by two techniques: manual digitization or skin markers. It was hypothesized that the body mass index or the skinfold thickness are significant variables in these relationships. The internal pelvic landmarks were obtained with a stereoradiographic method. Results showed that the external coordinates are generally statistically different from the internal ones; manual digitization of the landmark reduces the soft tissue artifacts compared to the use of skin markers. Different regression models were obtained according to the external acquisition method. Body mass index or skinfold thickness was generally included as a significant variable in models along the direction of the soft tissue thickness: postero-anterior direction for the anterior-superior iliac spine, medio-lateral direction for the apex of the iliac crests. With the use of skin markers, models obtained for a specific internal landmark coordinate include generally many variables, such as the other two coordinates of the landmark, body mass index, or skinfold measurements. This study presented preliminary results on the relationships between internal and external pelvic landmark coordinates. More research is needed before the full relationships are understood and adequate models are developed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.695

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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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 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

Citations7
Published2006
Admission routes1
Has abstractyes

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