Review: vaccination reduces the incidence of serologically confirmed influenza in healthy adults
Bibliographic record
Abstract
Demicheli V, Rivetti D, Deeks JJ, et al . Vaccines for preventing influenza in healthy adults. Cochrane Database Syst Rev 2004;(3):CD001269. Q Is vaccination effective for reducing the incidence of influenza in healthy people 14–60 years of age? ### ![Graphic][1] Data sources: Cochrane Central Register of Controlled Trials ( Cochrane Library , Issue 1, 2004), MEDLINE (1966–2003), EMBASE/Excerpta Medica (1990–2003), bibliographies of relevant articles, and manufacturers and researchers. ### ![Graphic][2] Study selection and assessment: controlled trials (published in any language) that evaluated the effectiveness of influenza vaccines for protection from exposure to naturally occurring influenza in healthy people 14–60 years of age. ### ![Graphic][3] Outcomes: incidence of clinically defined influenza (CDI) (unspecified or specified on the basis of specific symptoms or signs) and serologically confirmed influenza (SCI). 47 trials (25 randomised controlled trials [RCTs], n = 59 566) met the selection criteria. (1) Influenza vaccine (inactivated parenteral vaccine [IP], live aerosol … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif [3]: /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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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".