Total intracranial hemorrhage volume measurement summating all compartments best in traumatic and nontraumatic intracranial bleeding
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The ANNEXA-4 trial measured hemostatic efficacy of andexanet alfa in patients with major bleeding taking factor Xa inhibitors. A proportion of this was traumatic and nontraumatic intracranial bleeding. Different measurements were applied in the trial including volumetrics to assess for intracranial bleeding depending on the compartment involved. We aimed to determine the most reliable way to measure intracranial hemorrhage (ICrH) volume by comparing individual brain compartment and total ICrH volume. METHODS: Thirty patients were randomly selected from the ANNEXA-4 database to assess measurement of ICrH volume by compartment and in total. Total and compartmental hemorrhage volumes were measured by five readers using Quantomo software. Each reader measured baseline hemorrhage volumes twice separated by 1 week. Twenty-eight different ANNEXA-4 subjects were also randomly selected to assess intra-rater reliability of total ICrH volume measurement change at baseline and 12-h follow up, performed by three readers twice to assess hemostatic efficacy categories used in ANNEXA-4. RESULTS: Compartmental minimal detectable change percentages (MDC%) ranged between 9.72 and 224.13, with the greatest measurement error occurring in patients with a subdural hemorrhage. Total ICrH volume measurements had the lowest MDC%, which ranged between 6.57 and 33.52 depending on the reader. CONCLUSION: Measurement of total ICrH volumes is more accurate than volume by compartment with less measurement error. Determination of hemostatic efficacy was consistent across readers, and within the same reader, as well as when compared to consensus read. Volumetric analysis of intracranial hemostatic efficacy is feasible and reliable when using total ICrH volumes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".