Reviewer agreement in scoring 419 abstracts for scientific orthopedics meetings
Bibliographic record
Abstract
BACKGROUND: The selection of presentations at orthopedic meetings is an important process. If the peer reviewers do not consistently agree on the quality score, the review process is arbitrary and open to bias. The aim of this study was: (1) to describe the inter-reviewer agreement of a previously designed scoring scheme to rate abstracts submitted for presentation at meetings arranged by the Dutch Orthopedic Association; (2) to test whether the quality of reporting of submitted abstracts increased in the years after the introduction of the scoring scheme; and (3) to examine whether a review process with a larger workload had lower interrater agreement. METHODS: We calculated intraclass correlation coefficients (ICC) to measure the level of agreement among reviewers using the International Society of the Knee (ISK) quality-of-reporting system for abstracts. Acceptance rate and quality of the abstracts are described. RESULTS: Of 419 abstracts, 229 (55%) were accepted. Inter-reviewer agreement to rate abstracts was substantial (0.68; 95% CI: 0.47-0.83) to almost perfect (0.95; 95% CI: 0.92-0.97) and did not change over the eligible time period. A smaller proportion of abstracts were accepted after 2004. The mean ISK abstract score (with a maximum of 100 points) for accepted abstracts ranged from 60 (95% CI: 58-63) to 64 (95% CI: 62-66). The mean ISK abstract score for rejected abstracts varied from 46 (95% CI: 40-51) to 51 (95% CI: 47-55). Average scores for accepted and rejected abstracts did not change with time. The degree of workload of the reviewers did not influence their level of agreement. INTERPRETATION: The ISK abstract rating system has an excellent interobserver agreement. Other scientific orthopedic meetings should consider adopting this ISK rating system for further evaluation in a local or international setting.
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 | 0.401 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".