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Modeling the Human Lumbar Spine for Assessing Spinal Loads, Stability, and Risk of Injury

2003· review· en· W1987498021 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2003
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsSimon Fraser University
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsLumbar spineBiomechanicsComputer scienceStability (learning theory)Spinal injuryPhysical medicine and rehabilitationRisk analysis (engineering)MedicineSpinal cord injurySurgeryMachine learningAnatomy

Abstract

fetched live from OpenAlex

This article provides a critical review of biomechanical modeling approaches used to estimate spinal loads, stability, and risk of injury. The complete biomechanical analysis of the spine requires a two-stage approach: (1) equilibrium analysis for estimating tissue loads, and (2) stability analysis for estimating structural tolerance of the spine. For each level of analysis, basic principles are reviewed and relevant published models and studies are referenced. Throughout the article, implications for lumbar function and dysfunction derived from the various modeling methods are presented, and their applications are discussed. At the end, future directions for research are identified. Detailed descriptions of selected modeling approaches are provided in the appendices.

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.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.100
GPT teacher head0.437
Teacher spread0.337 · 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