Methodology for the Guidelines on Evaluation and Management of Hypoparathyroidism and Primary Hyperparathyroidism
Bibliographic record
Abstract
To develop guidelines for hypoparathyroidism and primary hyperparathyroidism, the panel assembled a panel of experts in parathyroid disorders, general endocrinologists, representatives of the Hypoparathyroidism Association, and systematic review and guideline methodologists. The guideline panel referred to a formal process following the Recommendations, Assessment, Development, and Evaluation Working Group (GRADE) methodology to issue GRADEd recommendations. In this approach, panelists and methodologists formatted the questions, conducted systematic reviews, evaluated risk of bias, assessed certainty of evidence, and presented a summary of findings in a transparent fashion. For most recommendations, the task forces used a less structured approach largely based on narrative reviews to issue non-GRADEd recommendations. The panel issued Eight GRADEd recommendations (seven for hypoparathyroidism and one for hyperparathyroidism). Each GRADEd recommendation is linked to the underlying body of evidence and judgments regarding the certainty of evidence and strength of recommendations, values and preferences, and costs, feasibility, acceptability and equity. This article summarizes the methodology for issuing GRADEd and non-GRADEd recommendations for patients with hypoparathyroidism or hyperparathyroidism. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.237 | 0.538 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.049 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".