Guidelines for submitting adverse event reports for publication
Bibliographic record
Abstract
Publication of case reports describing suspected adverse effects of drugs and medical products that include herbal and complementary medicines, vaccines, and other biologicals and devices is important for postmarketing surveillance. Publication lends credence to important signals raised in these adverse event reports. Unfortunately, deficiencies in vital information in published cases can often limit the value of such reports by failing to provide sufficient details for either (i) a differential diagnosis or provisional assessment of cause-effect association, or (ii) a reasonable pharmacological or biological explanation. Properly described, a published report of one or more adverse events can provide a useful signal of possible risks associated with the use of a drug or medical product which might warrant further exploration. A review conducted by the Task Force authors found that many major journals have minimal requirements for publishing adverse event reports, and some have none at all. Based on a literature review and our collective experience in reviewing adverse event case reports in regulatory, academic, and industry settings, we have identified information that we propose should always be considered for inclusion in a report submitted for publication. These guidelines have been endorsed by the International Society for Pharmacoepidemiology (ISPE) and the International Society of Pharmacovigilance (ISoP) and are freely available on the societies' web sites. Their widespread distribution is encouraged. ISPE and ISoP urge biomedical journals to adopt these guidelines and apply them to case reports submitted for publication. They also encourage schools of medicine, pharmacy, and nursing to incorporate them into the relevant curricula that address the detection, evaluation, and reporting of suspected drug or other medical product adverse events.
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 | 0.316 | 0.590 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.035 | 0.019 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.086 | 0.106 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".