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Record W2899338302 · doi:10.1177/0363546518800825

Can We Discriminate Symptomatic Hip Patients From Asymptomatic Volunteers Based on Anatomic Predictors? A 3-Dimensional Magnetic Resonance Study on Cam, Pincer, and Spinopelvic Parameters

2018· article· en· W2899338302 on OpenAlexaff
Vasco Mascarenhas, Paulo Rego, Pedro Dantas, António Caetano, Lennart Jans, Reto Sutter, Rui Mateus Marques, Olufemi R. Ayeni, José Guimarães Consciência

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAsymptomaticMagnetic resonance imagingMedicinePincer movementRadiologyOrthodonticsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Given the high prevalence of patients with hip deformities and no ongoing hip dysfunction, understanding the anatomic factors predicting the symptomatic state is critical. One such variable is how the spinopelvic parameters (SPPs) may interplay with hip anatomic factors. HYPOTHESIS/PURPOSE: SPPs and femoral- and acetabular-specific parameters may predict which patients will become symptomatic. The purpose was to determine which anatomic characteristics with specific cutoffs were associated with hip symptom development and how these parameters relate to each other. STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 2. METHODS: 548 participants were designated either symptomatic patients (n = 176, scheduled for surgery with hip pain and/or functional limitation) or asymptomatic volunteers (n = 372, no pain) and underwent 3-dimensional magnetic resonance imaging. Multiple femoral (α angle, Ω angle, neck angle, torsion), acetabular (version, coverage), and spinopelvic (pelvic tilt, sacral slope [SS], pelvic incidence) parameters were measured semiautomatically. Normative values, optimal differentiating thresholds, and a logistic regression analysis were computed. RESULTS: Symptomatic patients had larger cam deformities (defined by increased Ω angle and α angle), smaller acetabular coverage, and larger pelvic incidence and SS angles compared with the asymptomatic volunteers. Discriminant receiver operating characteristic analysis confirmed that radial 2-o'clock α angle (threshold 58°-60°, sensitivity 75%-60%, specificity 80%-84%; area under the curve [AUC] = 0.831), Ω angle (threshold 43°, sensitivity 72%, specificity 70%; AUC = 0.830), acetabular inclination (threshold 6°, sensitivity 65%, specificity 70%; AUC = 0.709), and SS (threshold 44°, sensitivity 72%, specificity 75%; AUC = 0.801) ( P < .005) were the best parameters to classify participants. When parameters were entered into a logistic regression, significant positive predictors for the symptomatic patients were achieved for SS, acetabular inclination, Ω angle, and α angle at 2-o'clock, correctly classifying 85% of cases (model sensitivity 72%, specificity 91%; AUC = 0.919). CONCLUSION: Complex dynamic interplay exists between the hip and SPPs. A cam deformity, acetabular undercoverage, and increased SPP angles are predictive of a hip symptomatic state. SPPs were significant to discriminate between participants and were important in combination with other hip deformities. Symptomatic patients can be effectively differentiated from asymptomatic volunteers based on predictive anatomic factors.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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 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

Citations54
Published2018
Admission routes1
Has abstractyes

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