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Roentgen single-plane photogrammetric analysis (RSPA)

2002· article· en· W4241788442 on OpenAlexafffund
Xia Yuan, Leif Ryd, K.E. Tanner, Lars Lidgren

Bibliographic record

VenueJournal of Bone and Joint Surgery - British Volume · 2002
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsRobarts Clinical Trials
FundersQueen's UniversityLunds UniversitetMedical Research CouncilStiftelsen för Strategisk Forskning
KeywordsPhotogrammetryRoentgenComputer visionFocus (optics)Position (finance)Computer scienceArtificial intelligenceMovement (music)Plane (geometry)OpticsMathematicsPhysicsAcousticsNuclear medicineMedicineGeometry

Abstract

fetched live from OpenAlex

We present a new approach for the accurate reconstruction of three-dimensional skeletal positions using roentgen single-plane photogrammetric analysis (RSPA). This technique uses a minimum of three markers embedded in each segment which allow continuous, real-time, internal skeletal movement to be measured from single-plane images, provided that the precise distance between the markers is known. A simulation study indicated that the error propagation in this approach is influenced by focus position, object position, the number of control points, the accuracy of the previous measurement of the distance between markers and the accuracy of image measurement. For reconstruction of normal movement of the knee with an input measurement error of sd = 0.02 mm, the rotational and translational differences between reconstructed and original movement were less than 0.27° and 0.9 mm, respectively. Our results showed that the accuracy of RSPA is sufficient for the analysis of most movement of joints. This approach can be applied in combination with force measurements for dynamic studies of the musculoskeletal system.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.052
GPT teacher head0.206
Teacher spread0.154 · 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
Published2002
Admission routes2
Has abstractyes

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