Location of intracerebral haemorrhage predicts haematoma expansion
Bibliographic record
Abstract
INTRODUCTION: The role of intracerebral haemorrhage location in haematoma expansion remains unclear. Our objective was to assess the effect of lobar versus non-lobar haemorrhage on haematoma expansion and clinical outcome. PATIENTS AND METHODS: We analysed data from the prospective PREDICT study where patients with intracerebral haemorrhage presenting to hospital under 6 h of symptom onset received baseline computed tomography (CT), CT angiogram, 24 h follow-up CT, and 90-day mRS. Intracerebral haemorrhage location was categorised as lobar versus non-lobar, and primary outcomes were significant haematoma expansion (>6 ml) and poor clinical outcome (mRS > 3). Multivariable regression was used to adjust for relevant covariates. The primary analysis population was divided by spot sign status and the effect of haemorrhage location was compared to haematoma expansion in exploratory post hoc analysis. RESULTS: Among 302 patients meeting the inclusion criteria, lobar haemorrhage was associated with increased haematoma expansion >6 ml (p = 0.003), poor clinical outcome (p = 0.011) and mortality (p = 0.017). When adjusted for covariates, lobar haemorrhage independently predicted significant haematoma expansion (aOR 2.2 (95% CI: 1.1-4.3), p = 0.021) and poor clinical outcome (aOR 2.6 (95% CI: 1.2-5.6), p = 0.019). Post hoc analysis showed that patients who were spot sign negative had a higher degree of haematoma expansion with baseline lobar haemorrhage (lobar 26% versus deep 11%; p = 0.01). No significant associations were observed in spot-positive patients (lobar 52% versus deep 47%; p = 0.69). DISCUSSION AND CONCLUSION: Haematoma expansion is more likely to occur with lobar intracerebral haemorrhage and haemorrhage location is associated with poor clinical outcome. As expansion is a promising therapeutic target, hemorrhage location may be helpful for prognostication and as a selection tool in future ICH clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".