Development of ACRODAT®, a new software medical device to assess disease activity in patients with acromegaly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Despite availability of multimodal treatment options for acromegaly, achievement of long-term disease control is suboptimal in a significant number of patients. Furthermore, disease control as defined by biochemical normalization may not always show concordance with disease-related symptoms or patient's perceived quality of life. We developed and validated a tool to measure disease activity in acromegaly to support decision-making in clinical practice. METHODS: An international expert panel (n = 10) convened to define the most critical indicators of disease activity. Patient scenarios were constructed based on these chosen parameters. Subsequently, a panel of 21 renowned endocrinologists at pituitary centers (Europe and Canada) categorized each scenario as stable, mild, or significant disease activity in an online validation study. RESULTS: From expert opinion, five parameters emerged as the best overall indicators to evaluate disease activity: insulin-like growth factor I (IGF-I) level, tumor status, presence of comorbidities (cardiovascular disease, diabetes, sleep apnea), symptoms, and health-related quality of life. In the validation study, IGF-I and tumor status became the predominant parameters selected for classification of patients with moderate or severe disease activity. If IGF-I level was ≤1.2x upper limit of normal and tumor size not significantly increased, the remaining three parameters contributed to the decision in a compensatory manner. CONCLUSION: The validation study underlined IGF-I and tumor status for routine clinical decision-making, whereas patient-oriented outcome measures received less medical attention. An Acromegaly Disease Activity Tool (ACRODAT) is in development that might assist clinicians towards a more holistic approach to patient management in acromegaly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it