MétaCan
Menu
Back to cohort
Record W1718728306 · doi:10.1002/hed.23510

Bone impacted fibular free flap: A novel technique to increase bone density for dental implantation in osseous reconstruction

2013· article· en· W1718728306 on OpenAlexaff
Peter T. Dziegielewski, Alex Mlynarek, Jeffrey Harris, Adam Hrdlicka, Brittany Barber, Khalid Al‐Qahtani, John F. Wolfaardt, Don Raboud, Hadi Seikaly

Bibliographic record

VenueHead & Neck · 2013
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsMcGill UniversityUniversity of Alberta
Fundersnot available
KeywordsDentistryImplantDental implantMedicineBone densityCortical boneSurgeryAnatomyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Fibular free flap (FFF) bone has thick cortical bone surrounding a fatty marrow. The cortex has sufficient density for dental implantation, but the marrow limits bone stock. A novel technique was devised to increase bone density: the bone-impacted fibular free flap (BIFFF). The purpose of this study was to: (1) describe the BIFFF technique; (2) evaluate the bone density of BIFFF; and (3) evaluate the stability/success of implants placed in BIFFFs. METHODS: Patients undergoing maxillary/mandibular reconstruction with FFFs were prospectively enrolled from 1998 to 2008. Two cohorts were compared: BIFFF and nonmodified FFF. The main outcome was bone density as seen on CT scans. Primary dental implant stability was determined via Periotest. RESULTS: Thirty-eight patients were included in this study. BIFFFs achieved higher bone density versus unmodified FFFs (p < .05). Greater primary dental implant stability occurred in BIFFFs (p < .05). One hundred percent of BIFFF and 59% of nonmodified FFF implants were successful at 1 year. CONCLUSION: BIFFF increases reconstructed bone density, initial dental implant stability, and 1-year implant success.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.942

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 head0.012
GPT teacher head0.268
Teacher spread0.255 · 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 designBench or experimental
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

Citations15
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueHead & NeckSame topicReconstructive Surgery and Microvascular TechniquesFrench-language works237,207