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Record W4387312274 · doi:10.1186/s12891-023-06908-7

Stricter correction of leg length discrepancy is required during total hip arthroplasty in patients with ankylosing spondylitis

2023· article· en· W4387312274 on OpenAlexaboutno aff
Chae-Jin Im, Chan Young Lee, Jae Young Beom, Min-Gwang Kim, Taek‐Rim Yoon, Kyung-Soon Park

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsAnkylosing spondylitisMedicineWOMACOsteoarthritisGaitPhysical therapyOrthopedic surgerySpondylitisHarris Hip ScoreRheumatologyPatient satisfactionSurgeryArthroplastyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with ankylosing spondylitis often have fusions in the spine and sacroiliac joints, such that it is difficult to compensate for leg length discrepancy (LLD). METHODS: We retrospectively measured the LLD after total hip arthroplasty (THA) in 89 patients with ankylosing spondylitis from June 2004 to February 2021 at our institute. Patients were divided into two groups based on an LLD of 5 mm. Clinical outcomes were investigated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Harris Hip Score (HHS). In addition, these points are investigated: patient satisfaction with the operation; whether there was a current difference in leg length; and whether there was a limping gait. RESULTS: The group with an LLD of 5-10 mm rather than < 5 mm had significantly worse WOMAC pain and stiffness. The survey revealed statistically significant differences in patient satisfaction with the operation, limping gait, and whether back pain had improved. CONCLUSION: For patients with ankylosing spondylitis, reducing the LLD to < 5 mm, which is more accurate than the current standard of < 10 mm, may produce greater improvement in clinical outcomes after hip arthroplasty.

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 score0.032
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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