Inspiring New Science to Guide Healthcare in Turner Syndrome: Rationale, design, and methods for the InsighTS Registry
Bibliographic record
Abstract
Inspiring New Science to Guide Healthcare in Turner Syndrome (InsighTS) Registry is a national, multicenter registry for individuals with Turner syndrome (TS) designed to collect and store validated longitudinal clinical data from a diverse cohort of patients with TS. Herein, we describe the rationale, design, and approach used to develop the InsighTS registry, as well as the demographics of the initial participants to illustrate the registry's diversity and future utility. Multiple stakeholder groups have been involved from project conceptualization through dissemination, ensuring the registry serves the priorities of the TS community. Key features of InsighTS include recruitment strategies to facilitate enrollment of participants that appropriately reflect the population of individuals with TS receiving care in the US, clarity of data ownership and sharing, and sustainability of this resource. The registry gathers clinical data on diagnosis, treatment, comorbidities, health care utilization, clinical practices, and quality of life with the goal of improving health outcomes for this population. Future directions include multiple patient-centered clinical-translational research projects that will use the InsighTS platform. This thorough and thoughtful planning will ensure InsighTS is a valuable and sustainable resource for the TS community for decades to come.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.287 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".