Prevalence, risk factors, and repair mechanism of different forms of sinus membrane perforations in lateral window sinus lift procedure: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To evaluate prevalences, affecting risk factors and efforts for repair mechanism for different forms of sinus membrane perforations (SMP) during sinus floor elevation (SFE) using the lateral window technique (LWT). MATERIAL AND METHODS: For 334/434 patients, SFE undergoing LWT prevalence of SMP was retrospectively evaluated including a subselection based on membrane perforation size (<10 mm: small-moderate/≥10 mm: large) and biotype (BT; thick BT/thin BT) into four subgroups (SMP1: thick BT/small-moderate; SMP2: thin BT/small-moderate; SMP3: thick BT/large; SMP4: thin BT/large). For the various subgroups, patient- and surgery-related/anatomic risk factors affecting SMP were evaluated and the scope of sinus membrane repair (SSMR) mechanisms rated with 1 (easy) to 5 (complex) was compared. RESULTS: For 103/434 SMP (27.6%) in 93/334 patients (30.8%) the prevalence of various forms of SMP differed significantly (p < 0.001) among the four subgroups. SMP4 with a prevalence of 45.6% (n = 47) was the most frequent type, while SMP3 had low prevalence with 4.85% (n = 5). Small/moderate SMPs with thick (SMP1: n = 26) or thin BT (SMP2: n = 23) were seen in 26.2% and 23.3%, respectively. Univariate analysis showed significant differences between subgroups with large perforations (SMP3/SMP4) and those with small/moderate perforations (SMP1/SMP2) regarding anatomic risk factors such as residual ridge height (p = 0.023) and history of previous oral surgical interventions (OSI; p = 0.026). Most evidently, multivariate analysis showed that induction of large SMP with thin biotype (SMP4) was significantly affected by the presence of sinus septa (p < 0.022, OR: 2.415), reduced residual ridge height (p < 0.001, OR: 1.842), and previous OSI (p < 0.001, OR: 4.545). SSMR differed significantly (p < 0.001) between SMP4 (4.62 ± 0.49) and the subgroups SMP1 (1.11 ± 0.32), SMP2 (1.08 ± 028), and SMP3 (2.2 ± 0.55). CONCLUSION: The most frequently found type of SMP had characteristics of thin biotype and large size associated with risk factors such as sinus septa, reduced residual ridge, and previous surgical interventions and required challenging repair mechanisms assessing clinical impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".