WCN25-2913 THE ROLE OF JOURNAL EDITORS AND AI IN UNITING SECTORS TO END THE ORGAN SHORTAGE IN TRANSPLANTATION OVER THE NEXT 10-15 YEARS
Bibliographic record
Abstract
between July 2023 to June 2024, we observed an increase in the overall usage of FFM overtime.However, the model usage varied between different clinics in Singapore.Eleven out of 33 clinics (33.3%) demonstrated minimal utilization of the FFM predictions.Lastly, we also observed an increase in the physician's degree of agreement for validated AVF risk scores over time (Figure 1).Table 1: Number of records, fistula failure incidence, and ROC-AUC score for different countries:Conclusions: The AI-based FFM demonstrated a robust global predictive performance though with some variation across different clinical settings.The overall FFM performance in Singapore was acceptable, but localized adaptation is required to further improve the model's performance.Fine-tuning the FFM to meet the needs of various clinical settings will help reduce time-consuming procedures and costs, while achieving better outcomes for patients.I have potential conflict of interest to disclose.This research is funded by Fresenius Medical Care.I did not use generative AI and AI-assisted technologies in the writing process.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.032 |
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".