Utility of simultaneous interventional radiology and operative surgery in a dedicated suite for seriously injured patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: In recent years, combined interventional radiology and operative suites have been proposed and are now becoming operational in select trauma centres. Given the infancy of this technology, this review aims to review the rationale, benefits and challenges of hybrid suites in the management of seriously injured patients. RECENT FINDINGS: No specific studies exist that investigate outcomes within hybrid trauma suites. Endovascular and interventional radiology techniques have been successfully employed in thoracic, abdominal, pelvic and extremity trauma. Although the association between delayed haemorrhage control and poorer patient outcomes is intuitive, most supporting scientific data are outdated. The hybrid suite model offers the potential to expedite haemorrhage control through synergistic operative, interventional radiology and resuscitative platforms. Maximizing the utility of these suites requires trained multidisciplinary teams, ergonomic and workplace considerations, as well as a fundamental paradigm shift of trauma care. This often translates into a more damage-control orientated philosophy. SUMMARY: Hybrid suites offer tremendous potential to expedite haemorrhage control in trauma patients. Outcome evaluations from trauma units that currently have operational hybrid suites are required to establish clearer guidelines and criteria for patient management.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".