Systematic Review of Factors Associated with Antibiotic Prescribing for Respiratory Tract Infections
Bibliographic record
Abstract
Antibiotic use is a modifiable driver of antibiotic resistance. In many circumstances, antibiotic use is overly broad or unnecessary. We systematically assessed factors associated with antibiotic prescribing for respiratory tract infections (RTI). Studies were included if they used actual (not self-reported or intended) prescribing data, assessed factors associated with antibiotic prescribing for RTIs, and performed multivariable analysis of associations. We searched Medline, Embase, and International Pharmaceutical Abstracts using keyword and MeSH (medical subject headings) search terms. Two authors reviewed each abstract and independently appraised all included texts. Data on factors affecting antibiotic prescribing were extracted. Our searches retrieved a total of 2,848 abstracts, with 97 included in full-text review and 28 meeting full inclusion criteria. Compared to other factors, diagnosis of acute bronchitis was associated with increased antibiotic prescribing (range of adjusted odds ratios [aOR], 1.56 to 15.9). Features on physical exam, such as fever, purulent sputum, abnormal respiratory exam, and tonsillar exudate, were also associated with higher odds of antibiotic prescribing. Patient desire for an antibiotic was not associated or was modestly associated with prescription (range of aORs, 0.61 to 9.87), in contrast to physician perception of patient desire for antibiotics, which showed a stronger association (range of aORs, 2.11 to 23.3). Physician's perception of patient desire for antibiotics was strongly associated with antibiotic prescribing. Antimicrobial stewardship programs should continue to expand in the outpatient setting and should emphasize clear and direct communication between patients and physicians, as well as signs and symptoms that do and do not predict bacterial etiology of upper respiratory tract infections.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".