Do No Harm: Lessons Learned About Contextualizing Your Findings
Bibliographic record
Abstract
Background. Post-marketing surveillance of adverse events following vaccination is necessary to monitor vaccine safety. We published a peer-reviewed paper on adverse events following HPV vaccination that included data on emergency department (ED) visits or hospitalizations within 42 days of vaccination. Our findings reported that of the 195,270 women that received 528,913 doses HPV of vaccine, 958 were hospitalized and 19,351 had an ED visit within 42 days of vaccination. We concluded that rates of adverse events after HPV vaccination in Alberta are low and consistent with types of events seen elsewhere. After publication all members of the research team received unusual emails and interview requests. Methods. We describe the sequence of events, hypotheses of the cause of these events, and actions taken. Results. The paper was published online February 26, 2016 with the first inquiry from a Canadian journalist on February 29. In April, two “independent” journalists emailed each research team member requesting an interview. Both emails used similar wording implying that ED utilization was high. By the end of April, a UK tabloid journalist requested an interview and two academic researchers emailed requesting data. The article scored in the 97th percentile of article of the same age (60 days) and source for comments/responses: 95 tweets from nine countries (88 from the public, 5 from science community and 2 from bloggers). Most of the social media comments noted that “9.9% ended up in emergency rooms in just 42 days” and attributed this directly to vaccination. We hypothesize that two factors this high rate of social media commentary: the title of the paper, and the lack of contextualization of the health service utilization. The unusual attention necessitated a plan and comparative data to contextualize the reported hospitalization data. University of Calgary Media Relations assisted us to identify a contact person, standard responses and a communications plan. Conclusion. Our experience demonstrates the importance of contextualizing results to minimize the misinterpretation of study findings. Social media may be used to disseminate misinterpretations of research findings and researchers should prepare accordingly. Disclosures. All authors: No reported disclosures.
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 | 0.229 | 0.381 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.036 | 0.051 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.017 | 0.030 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".