Fever after Immunization: Current Concepts and Improved Future Scientific Understanding
Bibliographic record
Abstract
Fever is a common clinical complaint in adults and children with a variety of infectious illnesses, as well as a frequently reported adverse event following immunization. Although the level of measured temperature indicative of a "fever" was first defined in 1868, it remains unclear what role fever has as a physiologic reaction to invading substances, how best to measure body temperature and compare measurements from different body sites, and, consequently, how to interpret fever data derived from vaccine safety trials or immunization safety surveillance. However, even with many aspects of the societal, medical, economic, and epidemiologic meanings of fever as an adverse event following immunization (AEFI) still elusive, it is a generally benign--albeit common--clinical sign. By standardizing the definition and means of assessment of fever in vaccine safety studies, thereby permitting comparability of data, we hope to arrive at an improved understanding of its importance as an AEFI.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".