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Record W4407945250 · doi:10.1111/cid.70009

Digitally Guided Aspiration Technique for Maxillary Sinus Floor Elevation in the Presence of Cysts: A Case Series

2025· article· en· W4407945250 on OpenAlexvenueno aff
Yuansheng Jiang, Yujia Yang, Liya Chen, Yi Man

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2025
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMaxillary sinusImplantCone beam computed tomographyMaxillaDentistryRadiographySinus (botany)Dental implantReduction (mathematics)OrthodonticsSurgeryComputed tomography

Abstract

fetched live from OpenAlex

OBJECTIVES: Sinus floor elevation (SFE) is a widely established surgical procedure for dental implant placement in the atrophic posterior maxilla. However, the presence of maxillary sinus cysts (MSCs) can significantly complicate this intervention. This study presents and evaluates the efficacy and safety of the Digitally Guided Aspiration Technique (DGAT), a novel approach for managing MSCs during SFE procedures. MATERIALS AND METHODS: Implant survival and success rates were evaluated according to established criteria, and all complications were systematically documented. Three-dimensional measurements, including MSC volume, residual bone height (BH) surrounding the implants, and apical bone coverage, were obtained using cone beam computed tomography (CBCT). Marginal bone loss (MBL) was assessed through standardized periapical radiographs following prosthetic loading. The accuracy of implant positioning was evaluated by measuring the three-dimensional deviations between virtually planned and actually placed implants. Comprehensive cytological and histological analyses were conducted on aspirated cystic fluid and harvested bone specimens, respectively. Patient-reported outcomes were assessed using questionnaires at the 6-month post-restoration follow-up. RESULTS: The study comprised seven patients with seven cysts receiving a total of 10 implants. At the 6-month follow-up, the implant survival rate was 100% with no biological or technical complications observed. Volumetric analysis revealed a significant mean reduction in MSC volume of 45.34% ± 33.08% (p = 0.012). Postoperative measurements demonstrated a statistically significant increase in BH compared to baseline values (p < 0.001). This gain remained largely stable throughout the 6-month observation period, with minimal resorption noted in the buccal aspect (p = 0.03) and mean value (p = 0.05). Prior to second-stage surgery, radiographic evaluation confirmed complete bone coverage of all implants, with 60% exhibiting > 2 mm of apical bone coverage. MBL remained within physiological limits. Analysis of implant positioning accuracy showed that coronal global and vertical deviations fell within acceptable clinical parameters, while apical global deviation and angular deviation marginally exceeded recommended thresholds. Cytological analysis of the aspirated cystic fluid revealed no evidence of infection, while histological examination of the regenerated tissue demonstrated mature bone formation with abundant vascularization. Patient-reported outcomes indicated high satisfaction levels. CONCLUSIONS: DGAT can reduce the volume of MSCs, achieve favorable bone grafting and dental implant outcomes with a low incidence of complications. The safety and effectiveness of this procedure need to be compared to the traditional aspiration technique in future randomized controlled trials. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2400083235. This clinical trial was not registered prior to participant recruitment and randomization.

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 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)0.000
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.129
GPT teacher head0.481
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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