Discrepancies between Proceedings Abstracts and Posters at a Scientific Meeting
Bibliographic record
Abstract
The proceedings handbook of abstracts from scientific meetings aims to provide meeting attendees with an accurate summary of scientific presentations. Given that posters are prepared closer to the meeting than the abstracts for the proceedings book, we hypothesized that there is a high rate of inconsistency between abstracts in the proceedings handbook and the corresponding posters. We compared the poster abstracts printed in the proceedings handbook with the actual posters at the 71st annual meeting of the American Academy of Orthopaedic Surgeons in 2004. Our comparison included all 50 trauma posters and 52 adult reconstruction knee posters. This comparison revealed discrepancies in 76% of the presented posters. These changes were detected in all parts of the posters including titles (33%), authorship (49%), methods (8%), results (30%), and conclusions (2%). The sample size changed in 15% of the studies. Discrepancies between the trauma posters versus the adult reconstruction knee posters were similar. Our findings suggest that discrepancies between the poster abstracts in the proceedings handbook and actual poster presentations are common, but changes in conclusions are rare. Meeting attendees should not assume that the proceedings handbook provides an accurate reflection of poster presentations. Visiting the poster section is recommended.
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 | 0.213 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".