Bibliographic record
Abstract
This review was performed to evaluate and discuss the quality and outcomes of studies assessing ultrasound imaging in pediatric regional anesthesia. Literature searches were conducted using MEDLINE and EMBASE, combining the search term "ultrasonography" with "regional anesthesia," "nerve block," "epidural anesthesia," and "spinal anesthesia," with the limit of 0 to 18 years. Additional literature was sought from departmental files and recent issues of several major anesthesiology journals. Meta-analyses/systematic reviews, randomized controlled trials, clinical studies without either randomization or control (eg, comparative studies), and case series (n > 10) were collected, reviewed, and graded for their quality (Jadad scores) and level of evidence (Grades of Recommendation). The search resulted in 211 total publications in pediatric literature, of which 12 were included in the evaluation of peripheral nerve blocks and 12 in the evaluation of neuraxial anesthesia. Although there is some evidence to support ultrasound for various outcomes in pediatric regional anesthesia, more randomized controlled studies with sufficient power are required to further support these findings and to evaluate the potential for ultrasound to reduce complications for regional anesthesia in children.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".