Low cerebral blood flow after cardiac arrest is not associated with anaerobic cerebral metabolism
Bibliographic record
Abstract
Aim of the study Estimation of cerebral anaerobic metabolism in survivors and non-survivors after cardiac arrest. Methods We performed an observational study in twenty comatose patients after cardiac arrest and 19 healthy control subjects. We measured mean flow velocity in the middle cerebral artery (MFV MCA ) by transcranial Doppler. Arterial and jugular blood samples were used for calculation of the jugular venous-to-arterial CO 2 /arterial to-jugular venous O 2 content difference ratio. Results After cardiac arrest, MFV MCA increased from 26.0[18.6–40.4]cm/sec on admission to 63.9[48.3–73.1]cm/sec after 72h (p<0.0001), with no significant differences between survivors and non-survivors (p=0.4853). The MFV MCA in controls was 59.1[52.8–69.0]cm/sec. The oxygen extraction fraction (O 2 EF) was 38.9[24.4–47.7]% on admission and decreased significantly to 17.3[12.1–26.2]% at 72h (p<0.0001). The decrease in O 2 EF was more pronounced in non-survivors (p=0.0173). O 2 EF in the control group was 35.4[32.4–38.7]%. The jugular bulb-arterial CO 2 to arterial-jugular bulb O 2 content difference ratio was >1 at all time points after cardiac arrest and did not change during admission, with no differences between survivors and non-survivors. Values in cardiac arrest patients were similar to those in normal subjects. Conclusions In this study, low CBF after cardiac arrest is not associated with anaerobic metabolism. Hypoperfusion appears to be the consequence of a decrease of neuronal functioning and metabolic needs. Alternatively, hypoperfusion may decrease cerebral metabolism. Subsequently, metabolism increases in survivors, consistent with resumption of neuronal activity, whereas in non-survivors lasting low metabolism reflects irreversible neuronal damage.
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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".