Scoping Review of the National Surgical Quality Improvement Program in Plastic Surgery Research
Bibliographic record
Abstract
BACKGROUND: The National Surgical Quality Improvement Program (NSQIP) is a robust, high-quality surgical outcomes database that measures risk-adjusted 30-day outcomes of surgical interventions. The purpose of this scoping review is to describe how the NSQIP is being used in plastic surgery research. METHODS: A comprehensive electronic literature search was completed in PubMed, Embase, MEDLINE, and CINAHL. Two reviewers independently reviewed articles to determine their relevance using predefined inclusion criteria. Articles were included if they utilized NSQIP data to conduct research in a domain of plastic surgery or analyzed surgical procedures completed by plastic surgeons. Extracted information included the domain of plastic surgery, country of origin, journal, and year of publication. RESULTS: journal published most of the (59%) NSQIP-related articles. All of the studies were retrospective. Of note, there were no articles on burns and only one study on trauma as the domain of plastic surgery. CONCLUSION: This scoping review describes how NSQIP data are being used to analyze plastic surgery interventions and outcomes in order to guide quality improvement in 106 articles. It demonstrates the utility of NSQIP in the literature, however also identifies some limitations of the program as it applies to plastic 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 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 | 0.115 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.052 | 0.057 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".