Quantifying adherence to growth hormone treatment: the easypod™ connect observational study (ECOS)
Bibliographic record
Abstract
Recombinant human growth hormone (r-hGH) is indicated for pediatric patients with a variety of growth disorders. Until recently, analysis of adherence to treatment has been limited by recall bias and reliance on self-reporting. Accurate recorded data on r-hGH use can now be collected using the easypod™ auto-injector. The multinational easypod™ connect observational study (ECOS) was launched in 2010 to collect and analyze r-hGH dosing, clinical and auxological data from patients prescribed r-hGH via easypod™. Twelve countries are currently recruiting patients. The primary objective is to assess adherence in patients receiving r-hGH via easypod™. Secondary objectives include describing the impact of adherence on clinical outcomes and identifying adherence patterns. Data will be obtained from patients’ medical notes and uploaded from auto-injectors. Auxological parameters are collected, and prescribed dosing data recorded at clinic visits as per routine clinical practice. Annual adherence will be calculated (number of days the patient administered injections divided by the expected number of injection days over 1 year, as a percentage). Dose intensity (total amount of dose received divided by planned amount of dose over 1 year, as a percentage) will be analyzed. Adherence data will be correlated with clinical outcomes. An adherence pattern will also be developed based on patients’ age, sex, indication, self-injection, and time on treatment. The study will run until 2015, with yearly analyses, and will be overseen by a multinational scientific steering committee. With data from ECOS, it will be possible to accurately assess r-hGH treatment adherence in various growth disorders and explore its potential impact on growth. Ultimately, drivers of and barriers to treatment adherence will be identified, allowing appropriate support programs to be developed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".