Pericapsular Nerve Group Block (PENG Block); Rising Value in Anesthesia
Bibliographic record
Abstract
ÖzObjective: Pericapsular nerve group block (PENG block) emerges in the search of effective methods in hip joint block.It was aimed to analyze the bibliometric analysis of publications related to this method and to examine the world literature.Methods: Articles, case reports, conference presentations, papers, and letters to the editor published in peer-reviewed journals published in the PubMed database about PENG block were examined.Results: This new block was first implemented in Canada, followed by Japan and India.It was seen that case series and original research started to be done worldwide, while only case reports were made from Turkey.It was seen that 65% of the publications on this subject were published in Science Citation Index (SCI) and SCI-Expanded (SCI-E) journals.While the average number of citations per publication related to the PENG block was 5.75 in SCI and SCI-E journals, it was found to be 1.42 in other indexed journals (p<0.05).The mean number of cases in the publications was higher in case series (17.07) in SCI and SCI-E journals than in other indexed journals (10.14) (p<0.05).It was revealed that more cases were required to publish case series in SCI and SCI-E journals (p<0.05). Conclusion:Although the method is new and effective, it is important that it be published in well-indexed journals for citation.We think that because of understanding why and how this block is implemented in which countries, the number of publications on this subject will increase in our country.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".