A Multi-Country Survey on the Availability of Intraoperative Use of Echocardiography for Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: This survey aimed to explore the availability and accessibility of echocardiography during noncardiac surgery worldwide. METHODS: An internet-based 45-item survey was sent, followed by reminders from August 30, 2021, to August 20, 2022. RESULTS: 1189 responses were received from 62 countries. Nearly seventy-one percent of respondents had intraoperatively used transesophageal or transthoracic echocardiography (TEE and TTE, respectively) for monitoring or examination. The unavailability of echocardiography machines (30.3%), lack of trained personnel (30.2%), and absence of clinical indications (22.6%) were the top 3 reasons for not using intraoperative echocardiography in noncardiac surgery. About 61.5% of participants had access to at least one echocardiography machine. About 41% had access to at least 1 TEE probe, and 62.2% had access to at least 1 TTE probe. Seventy-four percent of centers had a procedure to request intraoperative echocardiography if needed for noncardiac cases. Intraoperative echocardiography service was immediately available in 58% of centers. CONCLUSIONS: Echocardiography machines and skilled echocardiographers are still unavailable at many centers worldwide. National societies should aim to train a critical mass of certified TEE/TTE anesthesiologists and provide all anesthesiologists access to perioperative TEE/TTE machines in anesthesiology departments, considering the increasing number of older and sicker surgical patients scheduled for noncardiac surgery.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".