Factors associated with severe deep neck space infections: Targeting multiple fronts
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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