Antibiotic hypersensitivity and adverse reactions: management and implications in clinical practice
Bibliographic record
Abstract
BACKGROUND: Studies have shown the discrepancy between self-reported antibiotic allergies and true allergies. Inaccurate reporting of antibiotic hypersensitivities can limit treatment options and result in use of more expensive antibiotics and contribute to resistance. METHODS: This retrospective cohort chart review obtained data on 16,515 patients after obtaining IRB approval. Patients who had an antibiotic adverse reaction were identified, recorded, and their management reviewed. 7926 patients were selected from inpatient internal medicine clinics, 8042 patients from outpatient internal medicine clinics, and 547 from orthopedic clinics. RESULTS: The prevalence of reported antibiotic sensitivity in our study was 9.89% (n = 1624). Reported antibiotic sensitivity was 8.88% (n = 704) in inpatient settings as compared to 11.2% (n = 902) and 5.12% (n = 28) in medicine and orthopedic outpatient settings respectively. The top five antibiotic adverse reactions reported were penicillins (42%), sulfonamides (25%), fluoroquinolones (4.3%), tetracyclines (4.2%), and macrolides (3.5%). In all settings, penicillins and sulfonamides adverse reactions were the top two reportings. 11.88% (n = 193) of patients with reported adverse reactions reported sensitivities to multiple antibiotics. CONCLUSION: Our study demonstrated high prevalence of reported antibiotic sensitivity in three clinical settings. However, a significant portion of these patients may not be truly hypersensitive to these antibiotics. There is a need for increased awareness among medical professionals about the importance of detailed history taking and management of self-reported antibiotic allergies to combat unnecessary use of antibiotics.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".