Cumulative Radiation Dose during Hospitalization for Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Radiation exposure from neurointerventional procedures and diagnostic neuroimaging can be substantial, with many recommendations offered to guide the interventionalist in the conscientious use of ionizing radiation. Patients presenting with an aneurysmal subarachnoid hemorrhage can undergo multiple imaging procedures during a prolonged hospital course. Therefore, we reviewed a cohort of such patients to identify the sources and quantify the cumulative radiation exposure seen during their hospitalization. MATERIALS AND METHODS: We retrospectively reviewed a single-center experience with these patients to define the potential for short-term skin injury and long-term oncologic risk due to absorbed radiation dose and sources of ionizing radiation and their contribution to the cumulative absorbed dose to the cranial tissues. RESULTS: We demonstrated that substantial cumulative doses can be seen, with 87% of the cumulative absorbed dose occurring during neurointerventional procedures and 7% from CT. Mathematic modeling was performed identifying potential techniques to further reduce the cumulative radiation absorbed dose to these patients. CONCLUSIONS: We conclude that repetitive irradiation during the care of patients with aneurysmal subarachnoid hemorrhage can result in significant cumulative doses and a variety of techniques can be applied to reduce this absorbed dose. Use of radiation for diagnostic and therapeutic purposes during prolonged procedures of patients with subarachnoid hemorrhage demands diligence throughout the hospitalization.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".