Delay in Final Publication Following Abstract Presentation: American College of Veterinary Anesthesiologists Annual Meeting
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: A review of abstracts presented at nine annual meetings of the American College of Veterinary Anesthesiologists was undertaken to determine the average time to publication and the differences found between conference abstracts and final publications. Concerns about and advantages of using such abstracts in our teaching are considered. METHODOLOGY: Conference proceedings during the years 1990 through 1999 were considered. Key word and author searches using two common search engines were carried out to find whether abstracts presented had been published. The original article or its abstract was reviewed for consistency with the conference abstract. RESULTS: Of 283 abstracts examined, 73.5% were published in journals as full articles. The overall delay (+/-SD) in publication was 24.3 +/- 21.0 months. Common reasons for not publishing included too little time, more interest in carrying out the work than in writing it up, and other more demanding tasks. Authors indicated the intention of completing a submission on approximately 10% of the unpublished abstracts. The final articles reviewed showed major differences in key aspects from the abstract presented in 7% of the cases. In half of these cases, clinical action could have been affected by a change in emphasis of the conclusions. CONCLUSIONS: Because of the delay in publication of research, peer review of standardized abstracts should be encouraged. This material can be used to introduce students to new drugs, techniques, and results that may not otherwise become available until after their graduation. However, caution must be exercised in using this information, both because significant differences were noted in final publications and because unpublished research may be poorly interpreted at the time of presentation. This study emphasizes the value of critical review and lifelong learning in our careers.
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 | 0.195 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".