Prediction of cognitive response to surgery in elderly patients with primary hyperparathyroidism
Bibliographic record
Abstract
BACKGROUND: Primary hyperparathyroidism (pHPT) can be associated with potentially reversible cognitive impairment, which is occasionally mistaken for natural ageing and dementia. The aim was to evaluate short-term medical normalization of hypercalcaemia in surgical decision-making for elderly patients with mild cognitive deficiency. METHODS: Patients with pHPT were included in a prospective observational study. A test panel including the Montreal Cognitive Assessment (MoCA) and validated tools for estimation of psychological status (Hospital Anxiety and Depression Scale, HADS), and muscle strength (timed-stands test, TST) was applied at baseline, after 4 weeks of calcimimetic treatment, and after parathyroidectomy. Mild cognitive impairment was defined by a MoCA score below 26. A longitudinal increase in MoCA score of at least 2 points 6 months after surgery was considered clinically meaningful. RESULTS: Of 110 patients who underwent testing, 35 aged 50 years or more were identified to have mild cognitive dysfunction, including 19 who were aged at least 70 years (median MoCA score 23, i.q.r. 21-24). Calcimimetic treatment resulted in normalization of calcium levels, and improvements in MoCA and HADS scores, and TST time. Normal MoCA scores (at least 26) were reached in 17 patients by 6 months after surgery, of whom 10 were aged 70 years or older. Long-term increase in MoCA score correlated with the decrease in ionized calcium concentration (r = -0.536, P = 0.022). Baseline calcium concentration and improvement in MoCA with calcimimetic treatment were identified as independent predictors of favourable outcome after parathyroidectomy. CONCLUSION: Medical normalization of hypercalcaemia can aid in predicting outcome after parathyroidectomy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".