Minute Zero: an essential assessment in peri-operative ultrasound for anaesthesia
Bibliographic record
Abstract
In recent years, ultrasonography has gained unmatched importance in medical practice. After the initial use for central vascular access placement and regional anaesthesia, its application has expanded to airway, ocular, abdominal, lung and cardiac ultrasound, with the concept of point of care ultrasound (POCUS) gaining acceptability and applicability in the most diverse situations. In fact, it has recently been acclaimed as the fifth pillar to bedside evaluation [1]. Performing a POCUS-guided eva-luation has proved to be of value in emergency medicine, with studies demonstrating improved diagnosis and better outcomes [2]. Similarly, in critical care, systematic ultrasound evaluation has been shown to decrease the use of conventional diagnostic imaging tools and time on mechanical ventilation and improve the management of fluid therapy [3]. Recognition of the benefit of ultrasound evaluation in the perioperative period has been increasing. In fact, the need to master clinical ultrasound evaluation has led the Canadian anaesthesiology academic centres to issue recommendations regarding the scope of practice and required training for perioperative POCUS [4].
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 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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".