Evaluation of sleeping energy expenditure using the SenseWear Armband in patients with overt and subclinical hypothyroidism
Bibliographic record
Abstract
PURPOSE: The aim of the present study was to evaluate the average sleeping energy expenditure (EE) levels using the SenseWear Armband (SWA) in patients with overt and subclinical hypothyroidism. METHODS: Sixty patients with hypothyroidism and 30 healthy individuals were recruited for the study. Hypothyroid patients were divided into two groups: group 1 (n = 30) consisted of patients with overt hypothyroidism and group 2 (n = 30) consisted of patients with subclinical hypothyroidism. Lastly, group 3 (n = 30) consisted of healthy subjects. The average EE and metabolic equivalent of task (MET) values during sleep of all the hypothyroid participants were analyzed at baseline and at the end of the study. Data were also obtained from the healthy subjects at baseline. RESULTS: The average sleeping EE and METs values were not significantly different at baseline. Similarly, these values did not change significantly after achieving a euthyroid state via thyroid hormone replacement (both p > 0.05). CONCLUSIONS: Contrary to what has been previously reported , the average sleeping EE and METs values in all hypothyroid patients and healthy individuals were similar at baseline and did not change in the patients with overt and subclinical hypothyroidism after achievement of a euthyroid state.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".