The Potential Value of Video Abstracts to Dissemination of (Some) Information From National Association of Medical Examiners Annual General Meetings
Bibliographic record
Abstract
ABSTRACT: The purpose of this study is to determine whether video abstracts posted online to a social media platform (Twitter) increased dissemination of poster-abstract content from National Association of Medical Examiners (NAME) annual general meeting. At NAME meetings (2018, 2019), 20 authors accepted invitations to record visual abstracts. These were subsequently tweeted with their permission. Results were analyzed to determine how many times the video in each tweet was viewed and the number of impressions generated by the tweet. The NAME provided the number of attendees from each meeting to compare with online exposure to tweets.Video views per tweet ranged from 34 to 824 with a mean of 338. Of the 20 tweets, 5 (25%) had 600 or more views. The number of impressions per tweet ranged from 192 to 4629, with a mean of 1811. Seventy percent (14) had 500 or more impressions. Average conference participation for the meetings was 501.Given the numbers of views and even higher number of impressions, there appears to be a substantial increase in dissemination of poster abstracts beyond the conference attendees. The NAME should encourage authors of accepted abstracts to submit video summaries of their work. This will advertise forensic pathology research to a wider audience.
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 | 0.018 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".