Improving treatment outcomes through personalised medicine - assessment of disease activity in acromegaly.
Bibliographic record
Abstract
Purpose Personalised Medicine (PM), also known as stratified medicine has been known to improve treatment outcomes in a wide variety of disease area settings. Individualising treatment based on patient needs may also offer cost benefits to healthcare spend. 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. Methods An assessment to gauge the need to have an easy and helpful tool which may support acromegaly management was elucidated through a multinational qualitative survey. Subsequently, a validated a tool was developed to measure disease activity in acromegaly to support decision-making in clinical practice through a 2 step-approach. Firstly, 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. Secondly, a panel of 21 renowned endocrinologists at pituitary centres (Europe and Canada) categorized each scenario as stable, mild, or significant disease activity in an online validation study. Results The international qualitative survey revealed that current treatment practice does have shortcomings in fully achieving disease control as well as identifying the need for a helpful solution to guide acromegaly care. As part of elucidating the most important disease activity indicators, from expert opinion, five parameters emerged as the best overall indicators to evaluate disease activity: insulin-like growth factor I (IGF-I) level, tumour status, presence of comorbidities (cardiovascular disease, diabetes, sleep apnea), symptoms, and health-related quality of life. In the validation study, IGF-I and tumour 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 tumour size not significantly increased, the remaining three parameters contributed to the decision in a compensatory manner. Conclusion The validation study underlined the importance of IGF-I and tumour status for routine clinical decision-making, whereas patient-oriented outcome measures received less medical attention. A disease specific tool named Acromegaly Disease Activity Tool (ACRODAT) is in its final stages of development that will support clinicians in reviewing the disease activity in a holistic manner.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".