Risk factors for sinus membrane perforation during lateral window maxillary sinus floor elevation surgery: A retrospective study
Bibliographic record
Abstract
PURPOSE: To analyze the sinus membrane perforation (SMP) rate and its potential risk factors during lateral window maxillary sinus floor elevation (LSFE). MATERIALS AND METHODS: For patients with LSFEs at Department of Implantology, Stomatology Hospital, School of Medicine, Zhejiang Universitiy during January 2014 to December 2020, patient-related risk factors (age/sex/smoking habit), surgery-related risk factors (operator experiment/number of tooth units/technique of osteotomy/surgical approach), and maxillary sinus-related risk factors (residual bone height/sinus membrane thickness/lateral wall thickness/maxillary sinus contours/presence of septa/blood vessels at the lateral maxillary sinus wall) were compared between perforated and nonperforated sites and were evaluated for their influence affecting SMP. RESULTS: The study sample comprised 278 LSFE procedures in 278 patients; a total of 47 LSFE procedures (16.91%) presented SMP. Four significant factors were identified: smoking habit (p < 0.001), thin (≤1.5 mm) sinus membrane (p = 0.027), maxillary sinus contours (p < 0.001), and presence of septa (p = 0.001). The SMP rate of irregular, narrow tapered, and tapering sinus contours was significantly higher than that of ovoid and square one (p < 0.05). CONCLUSION: In general, smoking habit, thin sinus membrane, irregular, narrow tapered, and tapering sinus contours, and presence of septa may increase the risk of SMP during LSFE.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".