Determination of Biphasic Menstrual Cycle Based on the Fluctuation of Abdominal Skin Temperature during Sleep
Bibliographic record
Abstract
In this study, we focused on the fluctuation of the abdominal skin temperature (AST) during sleep as a second marker for determining the biphasic menstrual cycle, alongside the basal body temperature. The nocturnal AST was measured every 10 min using a wearable device mounted on the abdominal wall. With this system, the AST time-series data were recorded for a total of 1667, 1035, and 1690 days from seven participants for the menstrual/follicular, ovulatory, and luteal phases, respectively. First, the AST fluctuation was evaluated by plotting the cumulative probability distribution (CPD) of changes in AST every 10 min from 0 to 0.7℃. The results showed that the CPD fitted well with an exponential attenuation curve. Second, the mean attenuation coefficients obtained by exponential regression from the CPD data were compared among the three phases. For regular menstrual cycles, the attenuation coefficient was the highest in the menstrual/follicular phase (8.57; 95% confidence interval 8.44–8.70; R2 = 0.983; P < 0.001), followed by the ovulatory phase (7.80; 95% confidence interval 7.65–7.96; R2 = 0.985; P < 0.001) and then the luteal phase (7.24; 95% confidence interval 7.12–7.36; R2 = 0.985; P < 0.001). Finally, we examined whether the attenuation coefficients can be used as an index to classify the three phases by long short-term memory (LSTM)-based deep learning. Consequently, the attenuation coefficient affected the prediction of the menstrual/follicular, ovulatory, and luteal phases with significantly higher F-measures of 0.603, 0.328, and 0.660, respectively. These results suggest that the thermoregulatory system may increase the AST fluctuation in healthy women during the transition from the follicular phase to the ovulatory phase and then to the luteal phase.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".