Determinants of Length of Hospitalization due to Acute Odontogenic Maxillofacial Infections: A 2009-2013 Retrospective Analysis
Bibliographic record
Abstract
OBJECTIVES: To investigate the determinants of the length of hospitalization (LOH) due to acute odontogenic maxillofacial infections (AOMIs) from 2009 to 2013. MATERIALS AND METHODS: Dental records of adult patients with AOMIs and related data were retrieved from the Vilnius University's dental hospital. The LOH was related to several determinants in each of the following domains: outpatient primary care, severity of AOMIs, lifestyle and disease domains. Determinants were also associated with the LOH using multivariate analysis. RESULTS: A total of 285 patients were hospitalized with AOMIs, of which 166 (58.2%) were males and 119 (41.8%) were females. The mean LOH was 8.3 ± 4.9 days. The bivariate analysis did not reveal any statistically significant differences in LOH between patients with AOMIs who received urgent outpatient primary care and those who did not receive such care prior to hospitalization. All AOMI severity-related determinants were associated with the LOH. The LOH was related to coexisting systemic conditions but not to the higher severity of dental or periodontal diseases. Both bivariate and multivariate analyses revealed similar trends, where the most significant determinants of a longer LOH were related to the severity of AOMIs. CONCLUSION: The most important determinants regarding longer hospitalization were indicators of infection severity such as an extension of the odontogenic infection and the need for an extraoral incision to drain the infection.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".