The relationship between crying of premature infants with Monro-kellie hypothesis and increase of ventricular CSF Based on Doppler ultrasound findings
Bibliographic record
Abstract
Abstract Introduction: Infant crying causes an increase in intracranial pressure which is equivalent to a decrease in CSF and also a decrease in CSF before ischemic and hemorrhagic strokes observed. The object of this study is to evaluate the effect of crying on premature infant brain pressure and the effect of crying on brain autoregulation. Method: In a case-control study, the participants were 53 premature infants with the ability to cry and 43 non-crying premature. Apgar score and after birth blood gases were estimated, and 200 µl capillary samples were collected from the heel for assessment of blood gases before,during and after crying. A transcranial Doppler device used to measure cerebral blood flow volume (CBFV) levels and compared in three sections during, before, and after crying. Results: The CO2 higher level was during crying in comparison with after and before crying (P<0.001). The brain volume was enlarger during crying than after and before crying, as well (P<0.001). The Doppler ultrasound results showed that the higher resistive index (RI) and pulsatility index (PI) occurred during crying than after and before crying (P<0.001). There was the lowest end-diastolic velocity (EDV) and Peak systolic velocity (PSV) during crying than after and before crying (P=0.001).Conclusion: The results suggest that the brain volume has increased during crying, which is associated with simultaneous entry of CSF. In intracranial hemorrhage (IH), there is a decrease in CSF which is accompanied by a decrease in brain activity. Therefore, crying with an increased CSF and brain magnetic activity can probably prevent IH.
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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 | 0.003 |
| 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) | 0.002 | 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".