Factors associated with severe deep neck space infections: Targeting multiple fronts
Bibliographic record
Abstract
OBJECTIVES: To determine factors predictive of a severe deep neck space infection (DNSI), defined as those requiring surgery and/or postoperative intensive care unit (ICU) admission. To specifically examine dental practices and socioeconomic factors that may contribute to the development of a DNSI. STUDY DESIGN: Retrospective review. METHODS: This study was conducted at 2 tertiary care academic referral centers from January 2007 to September 2011. The study was composed of 2 arms: a prospective questionnaire and data collection to identify modifiable risk factors such as dental practices and socioeconomic considerations for a DNSI, and a retrospective review of deep neck space infections to identify commonly associated risk factors predictive of a severe DNSI, requiring surgery and/or postoperative ICU admission. RESULTS: 233 patients were reviewed retrospectively and 25 patients prospectively. Patients with a low level of education (p = 0.03), those living greater than 1 hour from a tertiary care center (p = 0.002), those that have tonsils (p = 0.03), and those with Streptococcus infections (p = 0.03) have an increase risk of developing a severe DNSI. Patients that were smokers (p = 0.02) or had diabetes (p = 0.02), and those that presented with airway compromise (p = 0.03) were more likely to have a prolonged hospital stay. CONCLUSIONS: Factors predictive of severe DNSIs are Streptococcus infections, the presence of tonsils, education level, and geographic location.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".