Predictors of longer hospitalization of maxillofacial infections‐a 17‐year retrospective study
Bibliographic record
Abstract
OBJECTIVES: To evaluate treatment outcomes in patients with severe maxillofacial infections requiring hospital care during a 17-year period. METHODS: A retrospective cohort study reviewed 5,465 medical records, and the following data were collected: the reason for infection, locations of inflamed regions, treatment provided, bacteriological findings, and treatment outcomes. Other information included sociodemographic characteristics (age, gender), presence of systemic diseases, and smoking history. RESULTS: The annual incidence rate of patients with acute maxillofacial infections was 206 ± 19 cases with a male to female ratio 1.4:1.0, a mean hospital stay of 7.9 ± 4.9 days. Older age (>65 years), smoking and systemic diseases (diabetes), the causative tooth (molar), and need for extraoral incision predicted longer hospitalization. Intravenous penicillin was the most common drug prescribed in 50.5% of cases. A total of 132 different microorganisms were identified. The highest microorganism resistance occurred for metronidazole and the highest sensitivity was to clindamycin. CONCLUSIONS: Increased age, smoking, diabetes, causative tooth, and the occurrence of several infected spaces were associated with a longer hospital stay. Streptococcus α haemolyticus was the most common microorganism found in more than 70.0% of cases that were sensitive to intravenous penicillin.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".