Ventilatory effect following 10 days concomitant exposure to neonatal intermittent hypoxia (NIH) and neonatal caffeine treatment (NCT) in 12‐days old rats
Bibliographic record
Abstract
Caffeine is regularly used to alleviate apneas in newborn infants. Its efficiency is thought to be related to increased respiratory response to hypoxia. However, we still lack knowledge on interactions between chronic NCT and NIH. Rat pups were exposed to NIH (nadir 5%O2, 6 cycles/h followed by 1h normoxia, 24h/day from P3‐12) and daily gavaged with caffeine (15 mg/kg; NIH+NCT) or water (NIH+NWT). Two other groups exposed to normoxia but gavaged with caffeine (NCT) or water (NWT) were used. At end of exposure, ventilation (VE ml/100g/min) was measured under baseline and in hypoxia (12% O2, 20 min) by plethysmography. Apnea frequency was assessed under baseline (>2 missed breaths). Baseline VE was: 194±18; 223±11*, 234±22* and 192±14 for NWT, NCT, NIH+NCT and NIH+NWT, respectively; *p0.05 vs NWT. Apnea frequency was significantly higher in NIH+NWT pups and lower in both NCT and NIH+NCT. The increase in VE was correlated with the decrease in apnea frequency (p=0.04). In response to hypoxia, the % increase from baseline was +2% for NCT; −10% for NIH+NWT; and +18% for NIH+NCT (p=ns). In conclusion: Chronic exposure to NCT in presence of NIH enhances baseline ventilation but not the hypoxic response. This suggests that caffeine by improving baseline VE acts to decrease apnea frequency. (CIHR, FRMI)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".