Reduced <scp>CBF</scp> recovery detected by longitudinal 3D‐<scp>SSP SPECT</scp> analyses predicts outcome of postoperative patients after subarachnoid haemorrhage
Bibliographic record
Abstract
The aim of this study was to evaluate the impact of cerebral blood flow (CBF) recovery obtained from brain single-photon emission computed tomography (SPECT) images on postoperative outcome after aneurysmal subarachnoid haemorrhage (SAH). Twenty-nine patients who had undergone surgical clipping for ruptured anterior communicating artery aneurysms were analyzed prospectively. Routine measurements of CBF were performed using technetium-99 m hexamethyl propyleneamine oxine SPECT on days 4 and 14 after SAH. Regional voxel data analyzed by three dimensional stereotactic surface projection (3D-SSP) were compared between patients and age-matched normal database (NDB). In 3D-SSP analysis of all patients, cortical hypoperfusion around the surgical site in bilateral frontal lobes was evident on day 4 (P < .05 vs NDB), which was improved significantly on day 14. However, the recovery was less complete in patients with poor clinical grades (P < .05) and presenting symptoms attributable to delayed cerebral ischaemia (DCI) (P < .05) than those without. Multivariate analysis showed that patients with mild to moderate CBF recovery (relative Z-score differences of <4) (P = .014; odds ratio, 2.5; 95% confidence interval, 1.93-3.31) was independently associated with poor functional outcome at 3 months. We conclude that reduced CBF recovery detected by serial 3D-SSP SPECT image analyses can be a potential predictor of poor prognosis in postoperative patients after SAH.
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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 | 0.001 |
| 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".