Variation in patient presentation and the use of computer tomography in the urgent care diagnosis and treatment of peritonsillar abscess
Bibliographic record
Abstract
Background A peritonsillar abscess (PTA) is a collection of infectious material within the peritonsillar space, seen most often in teenagers and young adults. The diagnosis of a PTA is reliant on a patient’s history and physical exam; however, CT scans continue to be used. The rationale for imaging in the diagnosis of a PTA may be better understood based on patient presenting symptoms and physical exam findings. Methods A retrospective review of adult patients diagnosed with a peritonsillar abscess in an acute care/emergency setting at a tertiary hospital between January 1 to December 31, 2019, was performed. Patients were arranged into two groups: those who underwent a CT scan versus patients who did not scan as part of their clinical work-up. Patient demographics, and differences in the rate of subjective and objective findings were compared. Results 43 patients were included in the study: 19 in the CT scan group, and 23 in the no-CT scan group. There was no statistically significant difference in the history of previous peritonsillar abscess incidence, patient chief complaint at triage, subjective complaints, or objective physical exam findings. The most common patient reported symptoms in both groups were odynophagia and dysphagia. The most common objective findings in both groups included peritonsillar fullness and erythema, and uvular deviation. Conclusion Patients who underwent a CT scan as part of their work-up for a peritonsillar abscess had no difference in symptoms or physical exam findings when compared to patients who did not have a CT scan.
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 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 | 0.001 | 0.016 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".