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A07 Intraoperative MRI

2012· article· en· W2333254470 on OpenAlexaboutno aff
Keith J. Ruskin, Hae Wone Chang

Bibliographic record

VenueEuropean Journal of Anaesthesiology · 2012
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntraoperative MRIInterventional magnetic resonance imagingWorkflowSedationMagnetic resonance imagingOperating theaterMedical physicsPatient safetyMedical emergencyHealth careSurgeryRadiologyDatabaseComputer science

Abstract

fetched live from OpenAlex

Background and Goals: Intraoperative MRI (IMRI) provides substantial benefits for the treatment of intracranial disease.1 Magnet safety underlies all aspects of patient care, but the unique workflow, limited access, and difficult communication make patient care extremely challenging. The MR OR is dark and noisy with multiple distractions, unfamiliar equipment, and limited access to the patient. This abstract outlines the unique hazards and our initial experience in developing an IMRI program. Methods: The Yale-New Haven Hospital MR OR opened in June 2010. The room was designed and built by IMRIS (Winnipeg, Manitoba, Canada). A Siemens 3 Tesla MRI is moved into the OR during surgery. Anesthesiologists use Invivo MRI physiologic monitors, GE Aestiva MRI anesthesia gas machines, and iRadimed infusion pumps. The neuroanesthesia group developed its own safety and training program. Checklists are used for all safety procedures. The MRI's built-in patient call system is used to call for help in an emergency. Training includes use of monitors and infusion pumps, workflow, and demonstrations of the magnet's strength. Neuroanesthesiologists and residents receive this training and undergo a short mentoring period. Results: 176 scans have been done since June 2010. IMRI is used to guide surgery and assess tumor resection. Active research protocols with the intraoperative MRI are underway. Conclusions and Discussion: All supplies and equipment are MR safe. MRI monitors and pumps were originally developed for patients receiving sedation for a diagnostic scan, and have a limited set of features. Room entry is through multiple locked doors, delaying help in an emergency. Team training improves communication and crisis management.2 Extensive cooperation between surgeons, nurses, anesthesiologists, and MRI personnel is required. Any team member can stop the scan if a problem occurs. We have developed a program to provide safe care for patients undergoing IMRI. References: Schulder M, Spiro D. Intraoperative MRI for stereotactic biopsy. Acta Neurochir Suppl. 2011;109:81–7. Leonard M, Graham S, Bonacum D. The human factor: the critical importance of effective teamwork and communication in providing safe care. Qual Saf Health Care. 2004 Oct;13 Suppl 1:i85–90.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.147
Threshold uncertainty score0.491

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1470.076

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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2012
Admission routes1
Has abstractyes

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