Reviewer agreement trends from four years of electronic submissions of conference abstract
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the inter-rater agreement between reviewers on the quality of abstract submissions to an annual national scientific meeting (Canadian Association of Emergency Physicians; CAEP) to identify factors associated with low agreement. METHODS: All abstracts were submitted using an on-line system and assessed by three volunteer CAEP reviewers blinded to the abstracts' source. Reviewers used an on-line form specific for each type of study design to score abstracts based on nine criteria, each contributing from two to six points toward the total (maximum 24). The final score was determined to be the mean of the three reviewers' scores using Intraclass Correlation Coefficient (ICC). RESULTS: 495 Abstracts were received electronically during the four-year period, 2001-2004, increasing from 94 abstracts in 2001 to 165 in 2004. The mean score for submitted abstracts over the four years was 14.4 (95% CI: 14.1-14.6). While there was no significant difference between mean total scores over the four years (p = 0.23), the ICC increased from fair (0.36; 95% CI: 0.24-0.49) to moderate (0.59; 95% CI: 0.50-0.68). Reviewers agreed less on individual criteria than on the total score in general, and less on subjective than objective criteria. CONCLUSION: The correlation between reviewers' total scores suggests general recognition of "high quality" and "low quality" abstracts. Criteria based on the presence/absence of objective methodological parameters (i.e., blinding in a controlled clinical trial) resulted in higher inter-rater agreement than the more subjective and opinion-based criteria. In future abstract competitions, defining criteria more objectively so that reviewers can base their responses on empirical evidence may lead to increased consistency of scoring and, presumably, increased fairness to submitters.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.488 | 0.673 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.156 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".