AcroVoice: eliciting the patients’ perspective on acromegaly disease activity
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: To determine how patients define acromegaly disease activity and treatment success and to quantify from the patients' perspective the relative importance of each disease parameter included in the ACRODAT®. METHODS: One hundred acromegaly patients on medical therapy (mean age = 47.1 years; SD = 11.96) completed an online preference study evaluating hypothetical patient profiles described in terms of insulin-like growth factor-I (IGF-I) levels, tumor size, comorbid conditions, signs/symptoms, and quality of life (QoL). Participants first completed a single-profile task experiment by rating 20 single patient profiles as exhibiting stable, mild, or significant disease activity based on treatment success. Next, participants completed a double-profile discrete choice experiment (DCE) by selecting the patient that was doing "better" from 15 profile pairs. Results were analyzed using logistic and conditional logistic models. RESULTS: When choosing between stable vs. mild or significant disease activity, signs/symptoms, tumor size, and IGF-I levels were weighted equally; IGF-I and signs and symptoms were valued equally when selecting mild vs. significant disease activity. The DCE showed that, statistically, all disease parameters, except comorbid conditions, predicted health status equally. Tumor size and IGF-I levels each accounted for 23% of the decision-making process; QoL, signs/symptoms, and comorbid conditions accounted for 21%, 19%, and 14%, respectively. CONCLUSION: All five ACRODAT® parameters had some influence on disease activity from the patients' perspective. To account for patients' preferences and optimize treatment and outcomes, a holistic disease management approach should be employed.
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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 | 0.001 |
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