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Record W4214847766 · doi:10.1097/scs.0000000000008610

Simultaneous Implant Placement and Sinus Lift Using Leukocyte-Platelet Rich Fibrin: A Retrospective 40-Month Study

2022· article· en· W4214847766 on OpenAlexaff

Bibliographic record

VenueJournal of Craniofacial Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsSinus liftMaxillary sinusSinus (botany)ImplantDental implantPlatelet-rich fibrin

Abstract

fetched live from OpenAlex

ABSTRACT: The aim of this study was to report the 40-month clinical radiographical outcomes of maxillary sinus floor augmentation using leukocyte-platelet rich fibrin (L-PRF) as a single graft in combination with immediately placed implants. Twenty partially edentulous individuals with missing teeth in the region associated to maxillary sinus were included in this retrospective study. A lateral sinus floor elevation was performed considering a complete wear of the bone wall of the maxillary sinus and immediately placing the implant and filled the maxillary sinus with L-PRF clots as a single graft. After 5 months, all the implants received single screwed implant-supported prostheses. Subsequent evaluation was every 4 months assessing the success rate and biological complications. Two maxillary sinus membrane were perforated and covered with L-PRF clots. The mean bone height gain was 62.6%, observing a 100% of success and that in all the cases achieved at least the implant height. In conclusion, the use of platelet-rich fibrin clot as a single graft biomaterial during the maxillary sinus lift procedure combined with immediate implant placement may be a reliable clinical alternative, even to repair the sinus membrane.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.642

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 head0.025
GPT teacher head0.285
Teacher spread0.260 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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