Hypoxic-ischaemic brain injury: imaging and neurophysiology abnormalities related to outcome
Bibliographic record
Abstract
BACKGROUND: The outcome for patients with hypoxic-ischaemic brain injury (HIBI) is often poor. It is important to establish an accurate prognosis as soon as possible after the insult to guide management. Clinical assessment is not reliable and ancillary investigations, particularly imaging and EEG, are needed to understand the severity of brain injury and the likely outcome. METHODS: We undertook a retrospective study of 39 patients on an intensive therapy unit (ITU) with HIBI who were referred for MRI. The patients were seen consecutively >57 months. HIBI was due to a variety of insults causing cardiac arrest, hypoperfusion or isolated hypoxia. RESULTS: The outcome was poor, 29 patients died, 7 were left severely disabled and only 3 made a good recovery. Characteristic imaging changes were seen on MRI. These included extensive changes in the cortex and the deep grey matter present on diffusion-weighted imaging (DWI) and T2-weighted imaging within 6 days of the insult. In other patients, different patterns of involvement of the cortex and basal ganglia occurred. There was no significant difference in the outcome or imaging appearances according to aetiology. A poor prognosis was consistently associated with a non- or poorly responsive EEG rhythm and the presence of periodic generalized phenomena with a very low-voltage background activity. CONCLUSION: In this retrospective study of patients with HIBI, MRI and EEG provided valuable information concerning prognosis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".