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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".