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Record W1983370077 · doi:10.1503/cjs.000812

Total hip arthroplasty using a combined anterior and posterior approach via a lateral incision in patients with ankylosed hips

2013· article· en· W1983370077 on OpenAlexvenueno aff
Jian Li, Zhiwei Wang, Ming Li, WU Yue-song, Weidong Xu, Zimin Wang

Bibliographic record

VenueCanadian Journal of Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicHeterotopic Ossification and Related Conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryAnkylosisHeterotopic ossificationTotal hip arthroplastyArthroplasty

Abstract

fetched live from OpenAlex

BACKGROUND: For most patients with severely ankylosed hips, traditional surgical approaches do not provide sufficient exposure during THAs. We report our experience with a combined anterior and posterior approach using a lateral incision for total hip arthroplasty (THA) in patients with severe, spontaneous bony hip ankylosis. METHODS: Between January 2004 and December 2008, patients with severe, spontaneous bony hip ankylosis underwent THA via a combined anterior and posterior approach using a lateral incision. RESULTS: We included 47 patients (76 hips) with a mean age of 53 (range 22-72) years in our study. All surgeries were successful, and no significant postoperative complications occurred. The mean operative duration was 1.5 (range 1.3-1.7) hours, and mean blood loss was 490 (range 450-580) mL. The mean duration of follow-up was 5.5 (range 2-11) years. Harris hip score improved from 53 to 88 points postoperatively, and the outcome was good to excellent in 88.37% of cases. Heterotopic ossification occurred in 6 hips, and infection, which resolved with antibiotics, occurred in 1 patient. CONCLUSION: This combined anterior and posterior approach to THA using a lateral incision in patients with severe, spontaneous ankylosis provides very good exposure, protects the abduction unit and results in good to excellent postoperative recovery.

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 score0.006
Threshold uncertainty score0.268

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)0.0000.000

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 head0.020
GPT teacher head0.211
Teacher spread0.191 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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