The pediatric glucocorticoid toxicity index
Bibliographic record
Abstract
To develop a Pediatric glucocorticoid toxicity index (pGTI), a standardized, weighted clinical outcome assessment that measures change in glucocorticoid (GC) toxicity over time. Fourteen physician experts from 7 subspecialties participated. The physician experts represented multiple subspecialties in which GCs play a major role in the treatment of inflammatory disease: nephrology, rheumatology, oncology, endocrinology, genetics, psychiatry, and maternal-fetal medicine. Nine investigators were from Canada, Europe, or New Zealand, and 5 were from the United States. Group consensus methods and multi-criteria decision analysis were used. The pGTI is an aggregate assessment of GC toxicities that are common, important, and dynamic. These toxicities are organized into health domains graded as minor, moderate, or major and are weighted according to severity. The relative weights were derived by group consensus and multi-criteria decision analysis using the 1000MindsTM software platform. Two quantitative scores comprise the overall toxicity profile derived from pGTI data: (1) the Cumulative Worsening Score; and (2) the Aggregate Improvement Score. The pGTI also includes a qualitative, unweighted record of GC side-effects known as the Damage Checklist, which documents less common toxicities that, although potentially severe, are unlikely to change with varying GC dosing. One hundred and seven (107) toxicity items were included in the pGTI and thirty-two (32) in the Damage Checklist. To assess the degree to which the pGTI corresponds to expert clinical judgement, the investigators ranked 15 cases by clinical judgement from highest to lowest GC toxicity. Expert rankings were then compared to case ranking by the pGTI, yielding excellent agreement (weighted kappa 0.86). The pGTI was migrated to a digital environment following its development and initial validation. The digital platform is designed to ensure ease-of-use in the clinic, rigor in application, and accuracy of scoring. Clinic staff enter vital signs, laboratory results, and medication changes relevant to pGTI scoring. Clinicians record findings for GC myopathy, skin toxicity, mood dysfunction, and infection. The pGTI algorithms then apply the weights to these raw data and calculate scores. Embedded logic accounts for the impact of age- and sex-related reference ranges on several health domains: blood pressure, lipid metabolism, and bone mineral density. Other algorithms account for anticipated changes in the height Z-scores used in the growth domain, thereby addressing a concern unique to GC toxicity in children. The Damage Checklist ensures comprehensive measurement of GC toxicity but does not contribute to pGTI scoring, because the scored domains emphasize manifestations of GC toxicity that are likely to change over the course of a trial. We describe the development and initial evaluation of a weighted, composite toxicity index for the assessment of morbidity related to GC use in children and adolescents. Developing the pGTI digital platform was essential for performing the nuanced calculations necessary to ensure rigor, accuracy, and ease-of-use in both clinic and research settings.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".