Association between maxillary sinus pathology and odontogenic lesions in patients evaluated by cone beam computed tomography. A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: A study is made of the association between maxillary sinus pathology and odontogenic lesions in patients evaluated with cone beam computed tomography. MATERIAL AND METHODS: A literature search was made in five databases and OpenGrey. Methodological assessment was carried out using the Newcastle-Ottawa tool for observational studies. The random-effects model was used for the meta-analysis. RESULTS: Twenty-one studies were included in the qualitative review and 6 in the meta-analysis. Most presented moderate or low risk of bias. The periodontal disease showed to be associated with the thickening of the sinus membrane (TSM). Mucous retention cysts and opacities were reported in few studies. The presence of periapical lesions (PALs) was significantly associated to TSM (OR=2.43 (95%CI:1.71-3.46); I2=34.5%) and to odontogenic maxillary sinusitis (OMS) (OR=1.77 (95%CI: 1.20-2.61); I2=35.5%). CONCLUSIONS: The presence of PALs increases the probability of TSM and OMS up to 2.4-fold and 1.7-fold respectively. The risk differences suggests that about 58 and 37 of out every 100 maxillary sinuses having antral teeth with PALs are associated with an increased risk TSM and OMS respectively. The meta-evidence obtained in this study was of moderate certainty, and although the magnitude of the observed associations may vary, their direction in favor sinus disorders appearance, would not change as a result.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".