Review: delaying a prescription reduces antibiotic use in upper respiratory tract infections
Bibliographic record
Abstract
Arroll B, Kenealy T, Kerse N. Do delayed prescriptions reduce antibiotic use in respiratory tract infections? A systematic review. Br J Gen Pract 2003;53:871–7.[OpenUrl][1][Abstract/FREE Full Text][2] Q In patients with upper respiratory tract infections (URTIs), is delaying a prescription effective for reducing antibiotic use? ### ![Graphic][3] Data sources: Medline (1966 to April 2003), EMBASE/Excerpta Medica, the Cochrane Controlled Trials Register, and researchers in the field. ### ![Graphic][4] Study selection and assessment: randomised controlled trials (RCTs) or clinical controlled trials (published in any language) that compared delayed and immediate antibiotic prescription for patients of any age with URTIs. URTIs included acute cough, sore throat, otitis media, the common cold, and sinusitis. Study quality was assessed using the Jadad scale. ### ![Graphic][5] Outcomes: use, consumption, or filling of prescriptions; and reported side … [1]: {openurl}?query=rft.jtitle%253DBritish%2BJournal%2Bof%2BGeneral%2BPractise%26rft.stitle%253Dbjgp%26rft.aulast%253DArroll%26rft.auinit1%253DB.%26rft.volume%253D53%26rft.issue%253D496%26rft.spage%253D871%26rft.epage%253D877%26rft.atitle%253DDo%2Bdelayed%2Bprescriptions%2Breduce%2Bantibiotic%2Buse%2Bin%2Brespiratory%2Btract%2Binfections%253F%2BA%2Bsystematic%2Breview.%26rft_id%253Dinfo%253Apmid%252F14702908%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=bjgp&resid=53/496/871&atom=%2Febnurs%2F7%2F3%2F75.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".