Design and development of a European registry for parathyroid carcinoma cases within the scope of the European Registries for Rare Endocrine Conditions (EuRRECa)
Bibliographic record
Abstract
Parathyroid carcinoma (PC) is an extremely rare malignant endocrine tumor of the parathyroid glands. Given the extreme rarity of this cancer, many issues regarding its diagnosis, prognosis, clinical management, and tumor-derived complications remain unresolved, and no standardized protocol has yet been defined for its diagnosis. As with all rare clinical conditions, the creation of multicenter national databases and international registries is fundamental in order to collect data on a relatively high number of PC cases and increase knowledge of this rare parathyroid condition, with the ultimate aim of identifying potential factors that influence its diagnostics, natural course, prognosis, skeletal complications, and treatment. In this light, the “parathyroid carcinoma working group” (https://eurreca.net/parathyroid-carcinoma/) designed and developed a specific PC module within the scope of the European Registries for Rare Endocrine Conditions (EuRRECa). The module, finalized and launched at the end of 2022, is now available online to collect data on PC cases. Users must first request access to the e-Reporting of Rare Conditions system (e-REC) and obtain their personal login credentials.
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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".