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Record W2941241694 · doi:10.1186/s12913-019-4040-2

Identifying barriers to the use of ultrasound in the perioperative period: a survey of southwestern Ontario anesthesiologists

2019· article· en· W2941241694 on OpenAlexafffundabout
Jason Chui, Ron Lavi, Ahmed Hegazy, Philip M. Jones, Ramiro Arellano, Hye Yeon Yang, Daniel Bainbridge

Bibliographic record

VenueBMC Health Services Research · 2019
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsWestern University
FundersAcademic Medical Organization of Southwestern Ontario
KeywordsMedicinePerioperativeAnesthesiologyNursing researchStaffingHealth informaticsMedical emergencyNursingPublic healthSurgeryAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: Ultrasound (US) can be used for many perioperative procedures, but evidence is lacking as to its frequency of use and barrier of application. The objectives of this survey were to determine i) how often US guidance was used perioperatively for vascular access placement, nerve blocks, and heart and lung assessment, and ii) to identify the barriers and the limitations of using US amongst anesthesiologists in southwestern Ontario. METHODS: We conducted a web-based survey in over 40 academic or community hospitals at southwestern Ontario. RESULTS: Of 266 surveys sent, 66 complete surveys were obtained (response rate of 25%). Most respondents (> 80%) reported that US was commonly used for central venous catheter (CVC) insertion, followed by regional blocks; the uses were less frequent for neuraxial blockade and cardiopulmonary assessment. Most respondents wanted to use US more frequently as part of their practice and felt that they already had adequate US training. However, most respondents (59%) reported limited access to US machines in their working institutes as being the major barrier to incorporating US in their daily practice. CONCLUSION: The most common uses of US in anesthesia practice in southwestern Ontario were for CVC insertion and regional blocks. Most anesthesiologists in southwestern Ontario are interested to incorporate US in their daily practice but most were limited by the lack of US resources. Apparently, only providing knowledge and skills teaching may not be sufficient to further improve the US utilization in our region; a matched administrative effort appears to be the next challenge.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.248
GPT teacher head0.474
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2019
Admission routes3
Has abstractyes

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