Development and evaluation of a quality score for abstracts
Bibliographic record
Abstract
BACKGROUND: The evaluation of abstracts for scientific meetings has been shown to suffer from poor inter observer reliability. A measure was developed to assess the formal quality of abstract submissions in a standardized way. METHODS: Item selection was based on scoring systems for full reports, taking into account published guidelines for structured abstracts. Interrater agreement was examined using a random sample of submissions to the American Gastroenterological Association, stratified for research type (n = 100, 1992-1995). For construct validity, the association of formal quality with acceptance for presentation was examined. A questionnaire to expert reviewers evaluated sensibility items, such as ease of use and comprehensiveness. RESULTS: The index comprised 19 items. The summary quality scores showed good interrater agreement (intra class coefficient 0.60 - 0.81). Good abstract quality was associated with abstract acceptance for presentation at the meeting. The instrument was found to be acceptable by expert reviewers. CONCLUSION: A quality index was developed for the evaluation of scientific meeting abstracts which was shown to be reliable, valid and useful.
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 | 0.180 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.029 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".