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Record W2047736826 · doi:10.11607/jomi.2793

A Retrospective Study of Complications Associated with 100 Consecutive Maxillary Sinus Augmentations via the Lateral Window Approach

2013· article· en· W2047736826 on OpenAlexaff
Hung‐Wen Lee, Wei‐Shao Lin

Bibliographic record

VenueThe International Journal of Oral & Maxillofacial Implants · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDehiscenceWound dehiscenceMaxillary sinusSinus (botany)SurgeryPerforationImplantMaxillaDentistrySinusitis

Abstract

fetched live from OpenAlex

PURPOSE: To report the complications encountered during 100 consecutive maxillary sinus augmentations via the lateral window approach and to propose solutions to manage these complications. MATERIALS AND METHODS: Pretreatment residual bone heights and the presence of septa were recorded. The incidences of any intraoperative or postoperative sinus complications such as excessive bleeding, membrane perforation, infection, wound dehiscence, sinusitis, loss of bone graft, and implant success were reported. results: This study evaluated 42 men and 44 women requiring 100 consecutive sinus elevation procedures between March 2008 and February 2011. Five intraoperative membrane perforations were noted, and one subsequently developed an active infection (2 weeks after surgery). Eight instances of suppuration were noted, while 10 sites presented with wound dehiscence 1 to 2 weeks after surgery. One example of partial loss of the bone graft 6 months after surgery was identified. Of a total of 151 implants placed in 97 sinuses (one patient with bilateral sinuses and one with unilateral sinus did not complete implant placement), 2 implants presented excessive bone loss prior to uncovering. One implant was removed and one was left submerged. conclusions: Sinus floor elevation utilizing the lateral window approach is a predictable approach to manage bone volume deficiency in the posterior maxilla for patients seeking dental implant-based treatment. However, complications may include membrane tear, infection, wound dehiscence, loss of graft, and implant failure. It is vital for the clinician to understand how to recognize and solve these complications.

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 score0.046
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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

Citations46
Published2013
Admission routes1
Has abstractyes

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