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Record W2572364682 · doi:10.1055/s-0036-1582667

Can EOS 3D Morphological Analysis Better Correlate with Pain Patterns than the Lenke Classification

2016· article· en· W2572364682 on OpenAlexaff
Leonardo Simões, Jean Ouellet, Fan Jiang, Sultan Aldebeyan, Ahmed Aoude, Jenny Sun, Catherine Ferland, Neil Saran, Sheila Bote

Bibliographic record

VenueGlobal Spine Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsMcGill University Health CentreShriners Hospitals for Children - CanadaMcGill University
Fundersnot available
KeywordsMedicineScoliosisDeformityIdiopathic scoliosisSpinal deformityBack painReferred painSurgeryPathology

Abstract

fetched live from OpenAlex

Introduction Adolescent Idiopathic Scoliosis (AIS) has been thought to be a relatively painless three dimensional (3D) deformity of the spine, but some studies has shown that the incidence of mild to moderate pain in AIS ranges from 25 to 50%. With the arrival of the new low dose radiation EOS technology, it is now possible to quantify intersegmental changes of the scoliotic spine in an upright position that could be related to pain. Hypothesis EOS 3D morphological analysis of AIS patients will better correlate with presence of pain than the Lenke classification. Methods Fifty-nine patients (7 male/52 female, mean age of 14.3 years old) who were scheduled for elective posterior spinal fusion with diagnosis of AIS. Preoperative clinical pain data recorded consisted of: 1) numerical visual analogue (VAS) pain scores quantifying patients average and worst pain over the preceding month; 2) SRS22 scores. Preoperative PA and lateral imaging were utilized to reconstruct and generate 3D models using the EOS software. Global and segmental intervertebral orientation in all three planes including the Da Vinci diagram identifying maximal deformity orientation were generated and correlated with patients' clinical presentation. In addition, standard curve magnitude, curve classification (Lenke) and pelvic parameters were also analyzed to observe their association to pain. Statistical analysis was performed with GraphPad Prism 6. Results Lenke classification and its subtypes did not correlate with pain, nor did any of the classic curve parameters, with the exception of the presence of hyper lumbar lordosis (>60 degrees) (r = 0.32, p = 0.06). Hyperlordotic patients reported greater pain intensity than normal lordotic patients (U = 81.50, p = 0.04). Additional new 3D parameters from both global and intersegmental vertebral orientations in space were investigated. With the exception of high intervertebral frontal, lateral tilt of L4 over L5, no 3D correlations with pain patterns were observed. Conclusion Despite the additional 3D morphological analysis generated by the EOS imaging, we were not able to identify anatomical characteristics associated to the pain experience reported by patients, with the exception of hyperlumbar lordosis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.644

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)0.0010.000

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 head0.277
Teacher spread0.253 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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