Diagnosis of Asymptomatic Primary Hyperparathyroidism: Proceedings of the Fourth International Workshop
Bibliographic record
Abstract
Asymptomatic primary hyperparathyroidism (PHPT) is a common clinical problem. The purpose of this report is to provide an update on the use of diagnostic tests for this condition in clinical practice. This subgroup was constituted by the Steering Committee to address key questions related to the diagnosis of PHPT. Consensus was established at a closed meeting of the Expert Panel that followed. Each question was addressed by a relevant literature search (on PubMed), and the data were presented for discussion at the group meeting. Consensus was achieved by a group meeting. Statements were prepared by all authors, with comments relating to accuracy from the diagnosis subgroup and by representatives from the participating professional societies. We conclude that: 1) reference ranges should be established for serum PTH in vitamin D-replete healthy individuals; 2) second- and third-generation PTH assays are both helpful in the diagnosis of PHPT; 3) normocalcemic PHPT is a variant of the more common presentation of PHPT with hypercalcemia; 4) serum 25-hydroxyvitamin D concentrations should be measured and, if vitamin D insufficiency is present, it should be treated as part of any management course; 5) genetic testing has the potential to be useful in the differential diagnosis of familial hyperparathyroidism or hypercalcemia.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".