Odontogenic Maxillofacial Infections: A Ten-Year Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: To analyze treatment modalities and results in patients with severe odontogenic maxillofacial infections during a 10-y period. METHODS: Medical records of 1,077 patients hospitalized because of severe odontogenic maxillofacial infections during 2003-2012 were reviewed. The sample consisted of the records that matched inclusion criteria. For each patient the following data were collected: Age, gender, presence of systemic diseases, length of hospital stay, causal tooth, location of inflammation, treatment, results of bacteriologic sampling, and anti-bacterial susceptibility. RESULTS: Male to female ratio was 1.4:1. Two or more anatomic spaces were involved in 42.9% of cases, 37.3% of which involved the floor of the mouth. Penicillin in combination with gentamicin or metronidazole was prescribed in 69% of cases. Sixty-two different micro-organism species were identified with predominance of Streptococcus haemolyticus (42.9%). The microbial analysis showed the highest susceptibility of predominant micro-organisms to penicillin was 76.9% and the highest resistance was to metronidazole (27.9%). CONCLUSIONS: The frequency of odontogenic maxillofacial infection remained almost unchanged during a 10-y period. Single-space infections were more common (57.1%) than infections involving two or more spaces. Susceptibility to penicillin remains relatively high; therefore, penicillin can remain part of the armamentarium for treatment of odontogenic maxillofacial infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".