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Record W4221006288 · doi:10.1097/bpb.0000000000000974

Top 100 cited studies in periacetabular osteotomy for acetabular dysplasia: do lower levels of evidence guide clinical practice?

2022· article· en· W4221006288 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Pediatric Orthopaedics B · 2022
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCohort studyProspective cohort studyRandomized controlled trialMEDLINEMeta-analysisEvidence-based medicineRetrospective cohort study

Abstract

fetched live from OpenAlex

As no prior study has examined the citations profile of key articles related to periacetabular osteotomy (PAO), our analysis utilized the Web of Science database to (1) identify the most-cited clinical studies relating to PAO in the management of acetabular dysplasia and (2) assess any trends over time with respect to the quality of literature. The top 100 highest-cited studies related to PAO had a mean of 49 citations (range, 6-666 per study). With respect to the level of evidence, most studies had level IV evidence (58%); 1% level I, 16% level II, 28% level III and 2% level V. Most studies were retrospective ( n = 86); there were 14 prospective studies (including one randomized study). The most common study designs were case series ( n = 58) and cohort ( n = 16), followed by matched-cohort ( n = 13) and case-control ( n = 6). The mean ± SD Newcastle-Ottawa Scale score was 6.48 ± 1.31. A total of 59 and 41 of the included articles were classified as high risk and high quality, respectively. No studies were classified as very high risk. As a whole, our analysis demonstrated that currently available PAO literature is still of low quality and of low level of evidence. While PAO has been well-documented as a durable procedure for addressing acetabular dysplasia, future research must focus on higher quality, randomized and prospective data to answer key clinical or technique-related topics.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.160
GPT teacher head0.448
Teacher spread0.288 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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