Patient-reported Perceptions, Experiences, and Preferences Around Intravenous and Oral Antibiotics for the Treatment of <i>Staphylococcus aureus</i> Bacteremia: A Descriptive Qualitative Study
Bibliographic record
Abstract
BACKGROUND: There is growing evidence to support partial oral antibiotic treatment of severe infections such as Staphylococcus aureus bacteremia, but clinical practice is slow to adopt this paradigm. We know little about how patients with severe infection experience and perceive intravenous and oral antibiotics in terms of quality of life and clinical effectiveness. We performed a qualitative study to elicit patients' views on treatment with intravenous and oral antibiotics, aiming to provide insights that could inform collaborative treatment decision-making. METHODS: We conducted semi-structured interviews with participants in the Staphylococcus aureus Network Adaptive Platform trial pharmacological sub-study PR-O-SNAP by telephone, in person, or via video conferencing. Interviews were recorded, transcribed and coded, and used to analyze and identify themes. Interviews occurred in 2 phases between November 2024 and January 2025, with interim analysis to refine interview questions between each phase. RESULTS: We interviewed 17 patients who had received sequential intravenous then oral antibiotics for treatment of Staphylococcus aureus bacteremia. Overall, most participants preferred oral antibiotics for their convenience, which enabled improved mobility and independence, despite a perception that oral regimens were more complex and likely to cause side effects, and that intravenous antibiotics were more effective. CONCLUSIONS: Choosing a route of antibiotic administration for treatment of severe infection is a nuanced decision which should incorporate not just a patient's clinical status but also their preferences and personal context. Patient convenience and functional goals should be considered in treatment discussions between clinicians and patients.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".