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Radiological comparison of acetabular anteversion on roentgenograms and computed tomograms functional outcomes in total hip arthroplasty

2021· article· en· W4205835021 on OpenAlexaboutno aff
Ayush Sharma, Mukand Lal, Sandeep Kashyap, Anupam Jhobta

Bibliographic record

VenueInternational Journal of Orthopaedics Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiological weaponAcetabulumRadiographyTotal hip arthroplastyWOMACOsteoarthritisHarris Hip ScoreOrthodonticsArthroplastyRadiologySurgery

Abstract

fetched live from OpenAlex

Introduction: Orientation and Alignment of prosthetic components are vitally important for the stability of total hip arthroplasty. Poor acetabular positioning is one of the many issues implicated with persistent pain due to impingement, dislocation, edge loading and liner fracture, which may be lead to patient dissatisfaction after total hip arthroplasty. Material and methods: Post-operative radiological analysis of the version of acetabulum through X-ray images and CT images was performed. Pre & post-operative scoring according to Modified Harris Hip Score (HHS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Oxford Hip Score (OHS) was documented. Results: A total of 55 patients were included in the study. The mean anteversion angle calculated on anteroposterior (AP) radiographs by Lewinnek’s method was 23.480 (Range 11 – 390) and on cross-table lateral radiograph by Woo and Morrey's method was 22.410 (Range16 – 560), compared to CT Scans measured was 28.640 (Range 11.10 – 50.100). Conclusion: Majority 69.09% of patients had excellent functional outcomes in a range of 11.1 – 360 of anteversion compared to Lewinnek’s safe zone. It suggests that there is flexibility in positioning the acetabular component than previously believed. If one has to err, it should be towards more anteversion. Infact to avoid dislocation, more anteversion is required to guard against unwarranted activities on part of the patient.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.041
GPT teacher head0.320
Teacher spread0.280 · 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
Published2021
Admission routes1
Has abstractyes

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