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Record W3042683436 · doi:10.1097/md.0000000000021246

Influence of prior hip arthroscopy on outcomes after hip arthroplasty

2020· review· en· W3042683436 on OpenAlexaboutno aff
Jiankuo Guo, Dongmei Dou

Bibliographic record

VenueMedicine · 2020
Typereview
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACHip arthroscopyArthroscopySurgeryArthroplastyOsteoarthritisCochrane LibraryComplicationHip surgeryHarris Hip ScoreHip arthroplastyMeta-analysisRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: No meta-analysis exists elucidate the impact of prior hip arthroscopy on the outcomes of subsequent hip arthroplasty. The purpose of this study was to systematically review the current evidence in the literature to ascertain if prior hip arthroscopy leads to inferior outcomes after hip arthroplasty. METHODS: PUBMED, Scopus, EMBASE, and Cochrane Library databases were searched on May 20, 2019. Only English publications were included. The primary outcomes were postoperative complication, revision, and reoperation. Secondary outcomes included Harris Hip Score (HHS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), operative time, and estimated blood loss. RESULTS: Eleven studies with a total of 1227 patients were included. The pooled data revealed that patients with prior hip arthroscopy were associated with higher risks for any postoperative complication (P < .00001), dislocation (P = .03), revision (P = .03), and reoperation (P = .02) following subsequent hip arthroplasty. However, there were no significant differences in infection rate (P = .22), HHS (P = .09), WOMAC (P = .19), operative time (P = .52), and estimated blood loss (P = .17) between the groups. CONCLUSIONS: Patients with prior hip arthroscopy are at increased risks for any postoperative complication, dislocation, revision, and reoperation following subsequent hip arthroplasty. However, prior hip arthroscopy does not appear to have an impact on the functional outcomes and intraoperative outcomes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.355
Teacher spread0.323 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2020
Admission routes1
Has abstractyes

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