Comparison of clinical and bacterial profile of odontogenic and non-odontogenic maxillofacial infections
Bibliographic record
Abstract
Introduction Deep carious lesions and their complications are possible causes of odontogenic infections. Although their location and clinical symptoms may mimic non-odontogenic infections, they are characterised by specific features that are helpful in their diagnosis and treatment. It seems worthwhile to create their clinical and microbiological profile. Aim of the research To compare the clinical and microbiological features of odontogenic and non-odontogenic infections. Material and methods The study was based on the medical records of 403 patients affected by the diseases. Results and conclusions There were statistically significant differences in the white blood cell count, the number of accompanying diseases, dysphagia and the occurrence of neck swelling, and the duration of hospitalisation between odontogenic and non-odontogenic infections. We identified the most common pathogens as well as the clinical parameters specific to these infections. Although bacterial distribution was similar in both groups with a predominance of aerobic cocci, non-odontogenic infections were characterised by a relatively high contribution of Staphylococcus aureus and Klebsiella pneumoniae in comparison to odontogenic infections. We also indicated submandibular and peritonsillar spaces as commonly involved fascial spaces in odontogenic and non-odontogenic infections, respectively. Circulatory diseases and connective tissue diseases were identified as a factor predisposing to odontogenic infections. Comorbidities are the most important risk factor for the development of odontogenic infections and their severe course requiring hospitalisation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".