Orthopaedic Trauma Association Annual Meeting Program Committee: Analysis of Impact of Committee Size and Review Process on Abstract Acceptance
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether scientific abstracts selected for podium presentation at the Orthopaedic Trauma Association (OTA) Annual Meeting differ based on the program committee size and/or the proportion of abstracts each committee member evaluates. METHODS: Abstract scores from the Orthopaedic Trauma Association program committee from 2010 through 2016 were obtained. All members (range, 8-9) reviewed each clinical abstract (range, 506-778) each year in a blinded fashion. The 90 top-scoring abstracts were considered "accepted" for this study. To determine the effect of reducing the committee size, all possible combinations of reviewers for each possible committee size were modeled. To determine the effect of reducing the number of abstracts each member reviewed, we used Monte Carlo simulation with 100 cycles to generate possible combinations of 1-9 reviewers for each abstract. Mean percent agreement with the actual selection was the primary outcome. RESULTS: The mean percent agreement progressively declined from 90.2% with 1 less committee member to 56.7% with only a single reviewer. For each reduction in the number of committee members, 4.4% agreement was lost. If all committee members were retained but the number of reviewers per abstract was reduced from 8 to 1, the mean percent agreement declined from 88.8% to 43.0%. Each reduction in reviewers per abstract reduced the mean percent agreement 6.3%. CONCLUSION: The findings inform program committees striving to balance the trade-off between an acceptable reduction in agreement, given a reduction in the program committee size or the proportion of abstracts each committee member evaluates.
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 | 0.374 | 0.708 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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; 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".