SAT-206 Development of a Minimum Dataset (MDS) for the Monitoring of Growth Hormone Therapy in Children with Prader Willi Syndrome (PWS) - A GloBE-Reg Initiative
Bibliographic record
Abstract
Abstract Disclosure: A. Leong: None. S. Chen: None. M. Alimussina: None. J. Bryce: None. M. Chen: None. A. Fu: None. E.F. Gevers: None. C. Hoybye: None. M. Hughes: None. V. Iotova: None. M.Y. Jalaludin: None. G.F. Kerkhof: None. F. Luo: None. J. Miller: None. O. Nyunt: None. E.F. Roche: None. M.G. Shaikh: None. T. Strong: None. M. Tauber: None. L.S. Tyszler: None. S.F. Ahmed: None. Introduction: Recombinant Human Growth Hormone (GH) was approved in the US (2000) and Europe (2001) for children with Prader-Willi Syndrome (PWS) to improve growth and body composition, with reported benefits in cognition, motor skills and behaviour. However, longer term safety and efficacy data of GH in PWS are still lacking and may be difficult to interpret due to lack of consistency in data collection among studies. This study aimed to identify the minimum dataset (MDS) that could be measured in a routine clinical setting across the world, to minimise burden on clinician data entry and improve quality of data collection to facilitate future studies on long term outcomes. Methods: The study was undertaken by the PWS Expert Working Group in GloBE-Reg, an international registry platform which supports studies on long-term safety and effectiveness of drugs. Twelve clinical experts on PWS from 10 countries and two patient representatives collaborated to develop this recommendation, based on previously published methodology (Chen et al. Horm Res Pediatr 2023). Data fields that achieved 70% consensus in terms of importance qualified for the MDS, provided <50% deemed the item difficult to collect. Several anomalies to the MDS rule were discussed to formulate the final MDS recommendation. Results: In total, 294 items were compiled from routine clinical practice with 33 redundant items removed and 261 items subjected to the grading system. 151/261 items achieved consensus as important data to collect when monitoring children with PWS on GH treatment, while 218/261 items were deemed easy to collect. Combining both the criteria for importance and ease of collection, 126 items fulfilled the MDS requirement. Four items were designated as core data, two were computed fields, five reassigned as non-MDS, 13 removed as unrelated to safety and effectiveness and 65 were merged into 18 fields. Several anomalies which did not fulfill MDS criteria were also extensively discussed to determine its validity within the MDS, in particular family history of Type 2 diabetes, change of GH therapy (if applicable) and adherence, to produce the final MDS recommendations of 58 items; of which 24 are only to be completed once. Conclusion: This exercise has identified by consensus the minimum dataset considered necessary, which can be collected through real-world data, to provide consistency and comparability in global studies for monitoring the safety and effectiveness of GH in children with PWS, applicable to the current daily preparations and potential newer long-acting GH. Presentation: Saturday, July 12, 2025
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".