Update on the adverse effects of antimicrobial therapies in community practice.
Bibliographic record
Abstract
OBJECTIVE: To gather information about antibiotic side effects to be used as a reference and learning resource for prescribing physicians. QUALITY OF EVIDENCE: A search of websites of various independent national agencies and recent review articles was performed. A summary table of adverse effects for each group of antimicrobials was then created, identifying allergies, short-term harms, and serious harms. The occurrence rate of each was listed when available. MAIN MESSAGE: Antimicrobials are necessary to treat various diseases. However, they cause adverse effects, such as allergic reactions, in addition to increased bacterial resistance. There is increasing awareness of the need to detect and evaluate adverse effects associated with medicines. Recently, severe and serious harms have been described for commonly used antibiotics. Therefore, current knowledge of harms from systemic oral antibiotics that are regularly used in family medicine is summarized in this article. CONCLUSION: It is difficult to identify and ascribe exact probabilities of most harms. However, all common antimicrobials create harms that must be considered when choosing whether to prescribe. Many adverse effects go unrecognized by prescribers. As side effects are inevitable, antimicrobials must be prescribed for as short a course as possible, only when the probability of benefit is greater than the risk of harm.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".