Diagnosis and treatment of intracranial hemorrhage in children with hemophilia
Bibliographic record
Abstract
Intracranial hemorrhage (ICH) is a significant complication for children with hemophilia. Identifying risk factors may allow us to establish clinically relevant guidelines for the diagnosis and management of ICH. The purpose of this review is to nucleate evidence from the available literature on the incidence, risk factors, presentation, treatment, and outcomes of ICH that can be utilized to develop a clinically useful framework for the diagnosis and management of hemophiliac patients with the condition. An electronic MEDLINE and EMBASE literature search was undertaken using the key words 'intracranial hemorrhage and hemophilia' and setting limits as: Last 10 years and Review or Randomized Controlled Trial (RCT) or Clinical Trial, or Practice Guidelines. Following review of all articles using predetermined search words and criteria, 31 were retrieved with sufficient data to address our objectives. An algorithm is presented for the management of children (≥3 years-18 years) with hemophilia and suspected ICH. A standardized approach to ICH may reduce unnecessary exposure to radiation via computed tomography scan in a select group of children. Currently there is limited scientific evidence to recommend a diagnostic and therapeutic algorithm for neonates with hemophilia.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".