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Record W2120662400 · doi:10.1002/hed.20776

Lateral oromandibular defect: When is it appropriate to use a bridging reconstruction plate combined with a soft tissue revascularized flap?

2008· article· en· W2120662400 on OpenAlexaff
Douglas B. Chepeha, Theodoros N. Teknos, Kevin Fung, Josef Shargorodsky, Assuntina G. Sacco, Brian Nussenbaum, Lamont Jones, Avraham Eisbruch, Carol R. Bradford, Mark E. Prince, Jeffrey S. Moyer, Julia S. Lee, Gregory T. Wolf

Bibliographic record

VenueHead & Neck · 2008
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSoft tissueBridging (networking)SurgerySignificant differenceDentistry

Abstract

fetched live from OpenAlex

BACKGROUND: A quasi-experimental retrospective study was undertaken to evaluate a new concept of free tissue volume restoration combined with bridging reconstruction plate (compartment approach) to reduce plate-related complication rates. METHODS: We evaluated 40 patients with large lateral mandible defects and associated complex soft tissue defects reconstructed with a revascularized soft tissue flap and titanium hollow screw reconstruction plates. A case-control comparison was performed based on reconstruction type: restoration of soft tissue defect (conventional approach-group 1) versus over-reconstruction of soft tissue defect (compartment approach-group 2). RESULTS: Plate exposure rate was 6 of 16 (38%) in group 1 versus 2 of 24 (8%) in group 2, and the difference was statistically significant (p = .04). The mean time to exposure was 10 months. Plate fracture rate was 6 of 23 (26.1%) in dentulous patients versus 1 of 17 (5.9%) in edentulous patients. Gastrostomy tube dependence was 6 of 16 (38%) in group 1 versus 6 of 24 (25%) in group 2. CONCLUSION: The "compartment approach" reduces plate exposure rate and gastrostomy tube dependence. Revascularized osseocutaneous reconstruction is still required in dentulous patients.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
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 head0.026
GPT teacher head0.259
Teacher spread0.232 · 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 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

Citations44
Published2008
Admission routes1
Has abstractyes

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