Comparison of clinical and bacterial profile of odontogenic and non-odontogenic maxillofacial infections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it