Knowledge representation in Health Research: the modeling of Adverse Events Following Immunization.
Bibliographic record
Abstract
Free-text reporting of Adverse Events Following Immunization (AEFIs) leads to inaccurate and incomplete data. Accurate representation of adverse event is a crucial part of clinical research: it may initiate further investigation of potential problems in vaccine safety or efficacy, and facilitate subsequent dissemination of safety-related information to the scientific community and the public [1,2]. However, current methods used for adverse events reporting are not sufficient, mitigating their usefulness. There is no standardization of the terminology used in the current Electronic Data Capture System used by Public Health Agency of Canada – at best a Medical Dictionary of Regulatory Activities (MedDRA [3]) code is assigned after parsing the clinician’s input, but this code is not linked to any definition. Several studies highlight the potential issues in using MedDRA for adverse event reporting, ranging from inaccurate reporting (as several terms are non-exact synonyms) to lack of semantic grouping features impairing processing in pharmacovigilance [4-8]. Additionally, only the final adverse event code as determined by the system is saved, and information about sub-parts are lost, therefore restricting ability of the physician to go back to the set of symptoms observed to establish the diagnostic, and limiting the ability to query the resulting datasets.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".