The Impact of an Ice Slurry-Induced Gastrointestinal Heat Sink on Gastrointestinal and Rectal Temperatures Following Exercise
Bibliographic record
Abstract
Gastrointestinal temperature (Tgint) measurement with a telemetric pill (TP) is increasingly used in exercise science. Contact of cool water with a TP invalidates Tgint assessment. However, what effect a heat sink created in the proximity of a TP may have on the assessment of Tgint remains unknown. We examined the impact of an ice slurry-induced heat sink on Tgint and rectal temperature (Trec) following exercise. After 20 min of seating (20–22 °C, 25–40% relative humidity (RH)), 11 men completed two intersperse exercise periods (31–32 °C, 35% RH) at 75–80% of estimated maximal heart rate until a Trec increase of 1 °C above baseline level. Following the first exercise period, participants were seated for 45 min and ingested 7.5 g·kg−1 of thermoneutral water, whereas, following the second period, they ingested 7.5 g·kg−1 of ice slurry. Both Tgint and Trec were measured continuously. The TPs were swallowed 10 h prior to the experiments. A bias ≤0.27 °C was taken as an indication that Tgint and Trec provided similar core temperature indices. Mean biases and 95% limits of agreement during passive sitting, first exercise, water ingestion, second exercise, and ice slurry ingestion periods were 0.16 ± 0.53, 0.13 ± 0.41, 0.21 ± 0.70, 0.17 ± 0.50, and 0.18 ± 0.66 °C, respectively. The rates of decrease in Tgint and Trec did not differ between the water and ice slurry ingestion periods. Our results indicate that ice slurry ingestion following exercise does not impact TP-derived assessment of Tgint compared with Trec.
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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 | 0.002 |
| 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".