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Record W4405946557 · doi:10.1002/hed.28059

Assessing Dental Implant Success: A Systematic Review and Meta‐Analysis of Primary Versus Secondary Implantation in Free Bone Flap Reconstruction for Malignant Tumors

2024· review· en· W4405946557 on OpenAlexaff
Sophie Dugast, Julie Longis, Marine Anquetil, Pierre Corre, Svetlana V. Komarova, Hélios Bertin

Bibliographic record

VenueHead & Neck · 2024
Typereview
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsMcGill UniversityShriners Hospitals for Children - CanadaMcGill University Health Centre
Fundersnot available
KeywordsMedicineOsteosarcomaImplantDentistryDental implantMeta-analysisPrimary boneSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Dental implantation of bone reconstructions in oncologic situations improves patients' orofacial function and quality of life. There are currently no recommendations on the timing of implantation. METHODS: This systematic review with meta-analysis aimed to compare primary and secondary dental implantation of free bone flaps in reconstructions for malignant tumors of the oral cavity. The primary objective was to evaluate the implant survival rate, with secondary criteria including time to prosthesis placement, postoperative complications, and data on quality of life. Results-Three databases were screened for articles published between January 1990 and April 2024. Out of 2438 studies, 16 met the eligibility criteria, encompassing 284 patients. In total, 319 implants were placed in the primary implantation group and 1108 in the second group. Implant survival rate was 92.5% in the primary implantation group compared to 88.5% in the secondary implantation group. This systematic review underscores a higher success rate for implants placed primarily in patients with oral cancer. CONCLUSIONS: Given the rapid functional and aesthetic improvement offered by prosthetic rehabilitation, primary implantation should be systematically considered in the oncological population.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0010.001
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 head0.073
GPT teacher head0.376
Teacher spread0.304 · 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 designSystematic review
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

Citations6
Published2024
Admission routes1
Has abstractyes

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