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Record W3037201058 · doi:10.1111/cid.12928

Dental implant‐based oral rehabilitation in patients reconstructed with free fibula flaps: Clinical study with a <scp>follow‐up</scp> 3 to 6 years

2020· article· en· W3037201058 on OpenAlexvenueno aff
Funda Göker, Alessandro Baj, Alessandro Bolzoni, Carlo Maiorana, Aldo Bruno Giannì, Massimo Del Fabbro

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2020
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFibulaMedicineRehabilitationImplantDentistryFree flapOral and maxillofacial surgerySurgeryReconstructive surgeryDental prosthesisOsseointegrationDental implantTibiaPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Oral rehabilitation of patients after maxillofacial reconstructive surgery represents a challenge and stable prosthetic retention can be achieved with the use of dental implants. PURPOSE: This retrospective report aimed to evaluate implant-based oral rehabilitation following maxillofacial reconstruction with free fibula flaps. MATERIALS AND METHODS: A total of 14 patients who had reconstruction with fibula flaps either by CAD/CAM or conventional surgery were included in this study. A total of 56 implants (40 in flaps, 16 in native bone) were evaluated. Follow-up after reconstructive surgery ranged between 3.25 and 6.3 years. Follow-up after implant surgery ranged between 1.5 and 3.8 years. RESULTS: Overall survival rate was 85.7% in free fibula flaps and 85.6% in dental implants. Eight implants were lost in three patients and all of these failures were in dental implants inserted in free flaps. According to the results on patient basis, the implant survival was not influenced by any variable. CONCLUSIONS: The maxillofacial reconstruction with free fibula flap and oral rehabilitation with implant-supported prostheses after ablative surgery can be considered as an effective and safe procedure with successful aesthetic and functional 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)0.003
metaresearch head score (Gemma)0.005
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 score0.017
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.068
GPT teacher head0.406
Teacher spread0.339 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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