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Record W2331096620 · doi:10.1097/mcc.0000000000000031

Utility of simultaneous interventional radiology and operative surgery in a dedicated suite for seriously injured patients

2013· review· en· W2331096620 on OpenAlexaff
Scott D’Amours, Pratik Rastogi, Chad G. Ball

Bibliographic record

VenueCurrent Opinion in Critical Care · 2013
Typereview
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsMedicineInterventional radiologySuiteMultidisciplinary approachDamage controlMedical physicsMedical emergencyRadiologySurgery

Abstract

fetched live from OpenAlex

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between delayed haemorrhage control and poorer patient outcomes is intuitive, most supporting scientific data are outdated.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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.882
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.247
GPT teacher head0.504
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations31
Published2013
Admission routes1
Has abstractyes

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