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Spontaneous Bone Formation in the Maxillary Sinus after Removal of a Cyst: Coincidence or Consequence?

2003· article· en· W2123523616 on OpenAlexvenueno aff
Stefan Lundgren, Sten Andersson, Lars Sennerby

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2003
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMaxillary sinusMedicineSinus (botany)CystBone graftingDentistrySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Maxillary sinus floor-augmentation techniques are frequently used to increase the bone volume in the posterior edentulous maxilla to enable placement and integration of titanium implants. PURPOSE: The purpose of this report is to document an unexpected healing pattern after maxillary sinus surgery and to discuss the implications for future bone-augmentation techniques. MATERIALS AND METHODS: In a patient referred for sinus augmentation, an intrasinus mucosal cyst was removed 3 months prior to the planned augmentation procedure. A replaceable bone window was prepared in the lateral aspect of the sinus wall. The cyst was removed, the ruptured mucosa was sutured, and the bone window was replaced, resulting in a secluded space in the sinus. RESULTS: After 3 months of healing, the space between the replaced bony window and the lifted sinus membrane was filled with newly formed bone. The surgical technique was repeated in a second patient and resulted in a similar bone reformation pattern. CONCLUSION: Surgical trauma and the creation of a secluded space between the bone surfaces and the sinus mucosa result in spontaneous bone formation in the maxillary sinus. The surgical approach described may be used to achieve bone reformation to enable placement of dental implants without the addition of any grafting material.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Case reportlow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Case reportlow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.119
GPT teacher head0.441
Teacher spread0.322 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations94
Published2003
Admission routes1
Has abstractyes

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