Patient‐based Surgical Outcome Tool Demonstrating Alleviation of Symptoms following Parathyroidectomy in Patients with Primary Hyperparathyroidism
Bibliographic record
Abstract
This study assessed the impact of parathyroidectomy on the preoperative symptoms of patients with primary hyperparathyroidism (1 degrees HPT) using a surgical outcome tool designed specifically for HPT. The multicenter nature of this study allowed us to validate further this disease-specific outcome tool. 1 degrees HPT patients from Canada, the United States, and Australia filled out the questionnaire preoperatively and postoperatively on day 7 and at 3 and 12 months. The symptoms recorded by the patients were expressed as parathyroidectomy assessment of symptoms (PAS) scores: the higher the score, the more symptomatic is the patient. Quality of Life (QOL) and self-rated health uni-scales were included. Altogether, 203 patients with 1 degrees HPT were enrolled; 27 from center A, 54 from center B, and 122 from center C; 58 nontoxic thyroid patients were enrolled for comparison. The comparison group had no significant change in their PAS scores throughout the study (scores 184, 215, 156, 186). All three centers demonstrated a significant reduction in symptoms following surgery. The median preoperative PAS score from center B patients was 282. Following surgery, PAS scores decreased significantly: 136, 58, 0 (p <0.05). Center C patients had a median preoperative PAS score of 344, decreasing postoperatively to 228 (p <0.05) and continuing to decrease to 190, then 180. Center A also demonstrated a significant reduction in symptoms at 3 months, from 510 preoperatively to 209 (p <0.001). Both QOL and self-rated health improved in the HPT patients, whereas no change was found in the comparison group following surgery. PAS scores are a reliable, disease-specific measure of symptoms seen with HPT. Parathyroidectomy significantly reduces these preoperative symptoms, and this change translated into an improved health-related QOL for the patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".