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Record W4206726805 · doi:10.1080/08869634.2021.2020995

Complications in intraoral versus external approach for surgical treatment of Eagle syndrome: A systematic review and meta-analysis

2022· review· en· W4206726805 on OpenAlexaff
Mário Serra Ferreira, Geovane Miranda, Fabiana T. Almeida, Giovanni Gasperini, Brunno Santos de Freitas Silva, José Valladares‐Neto, Joel Ferreira Santiago, Maria Alves Garcia Silva

Bibliographic record

VenueCRANIO® · 2022
Typereview
Languageen
FieldMedicine
TopicOropharyngeal Anatomy and Pathologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMeta-analysisChecklistCritical appraisalMedicineSystematic reviewMEDLINEPsychologyAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The surgical treatment of Eagle syndrome involves an intra- or extraoral approach. This systematic review set out to consolidate current knowledge on the prevalence of complications associated with intraoral and external approaches. METHODS: Seven main electronic and two gray literature databases were searched. Studies were blindly selected by two reviewers based on pre-defined eligibility criteria. Studies evaluating any type of complication in the treatment of Eagle syndrome were considered eligible. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for studies reporting prevalence data, case reports, and case series. The Comprehensive Meta-Analysis software (Software version 3.0 - Biostat, Englewood, NJ, USA) was used to perform all meta-analyses. RESULTS: Out of 1728 articles found on all databases, 36 were included for qualitative analysis. Twenty were included for quantitative analysis and meta-analysis. CONCLUSION: In this study, the highest rate of complications was found in the intraoral approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.026
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.286
GPT teacher head0.418
Teacher spread0.132 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations7
Published2022
Admission routes1
Has abstractyes

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