Sensitivity and Specificity of US and CT as Diagnostic Tools for Pediatric Lateral Neck Abscesses
Bibliographic record
Abstract
Abstract Objective Ultrasound (US) and computed tomography (CT) are commonly used in the diagnosis of pediatric neck abscesses. The objective of this study is to determine the sensitivity and specificity of US and CT in the diagnosis of pediatric lateral neck abscesses, with a secondary objective of evaluating the association of specific clinical features with a positive US or CT scan. Study Design Retrospective review of pediatric patients admitted to a tertiary care center from January 1, 2011, to December 31, 2020, with neck abscesses. Setting Tertiary care center. Methods The sensitivity and specificity of US and CT were calculated by comparing imaging performed within 24 h of incision and drainage (I&D). Multiple regression was used to evaluate the association of clinical features with a true positive US or CT. Results There were 171 patients included in this study, with a median age of 1.5 years (interquartile range [IQR]: 1‐5 years). I&D was done in 156 patients (91.2%), while 15 (8.8%) were treated with antibiotics. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of US were 69.5%, 80%, 96.6%, and 24.2%. The sensitivity, specificity, PPV, and NPV of neck CT were 95.5%, 80%, 95.5%, and 57.1%. Length of symptoms, skin erythema, and fluctuance were not significantly associated with a positive US ( F (3, 82) = 0.24, p = .9, R 2 = 0.01) or CT scan ( F (3, 30) = 0.84, p = .5, R 2 = 0.08). Conclusion Neck US has a low sensitivity for diagnosing pediatric neck abscesses, when compared to CT, but remains a useful initial investigation given its high PPV. Clinicians should have a low threshold for pursuing CT if there is a high suspicion of abscess formation. Level of Evidence: Level 4.
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.
How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".