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Record W2220550488 · doi:10.1002/hed.24315

Bone‐impacted fibular free flap: Long‐term dental implant success and complications compared to traditional fibular free tissue transfer

2015· article· en· W2220550488 on OpenAlexaff
Brittany Barber, Peter T. Dziegelewski, Richelle Chuka, Daniel A. O’Connell, Jeffrey Harris, Hadi Seikaly

Bibliographic record

VenueHead & Neck · 2015
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsMisericordia Community HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineFibulaFree flapImplantDental implantSurgeryDentistryHead and neckFree flap reconstructionRetrospective cohort studyTibia

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to compare complications and dental implant success between the bone-impacted fibula free flap (BIFFF) and the traditional fibular free flap used in mandibular and midface reconstruction. METHODS: Retrospective review of all patients undergoing BIFFF or traditional fibular free flap reconstruction from 2001 to 2009 was undertaken. Complications related to the BIFFF and traditional fibular free flap site of reconstruction were compared. Dental implant success rates for each type of flap were compared at 1-year intervals for 5 years. RESULTS: One hundred fourteen patients underwent 81 BIFFFs and 35 traditional fibular free flaps. No significant difference in complications between BIFFF (20.9%) and traditional fibular free flap (25.7%) reconstruction was observed. Logistic regression analysis revealed only the site as a predictor of both single and multiple complications. At 5 years postimplantation, dental implant success rates were 95.5% and 77.1% for BIFFF and traditional fibular free flap, respectively (p = .006). CONCLUSION: BIFFF reconstruction is a novel surgical technique that may improve long-term dental implant success rates with no additional risk of complications. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1783-E1787, 2016.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.303
Teacher spread0.254 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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