Current state and future direction of quality improvement in pediatric neurosurgery: a survey of pediatric neurosurgeons
Bibliographic record
Abstract
OBJECTIVE: Quality improvement (QI) is a methodology used to implement sustainable, meaningful change to improve patient outcomes. Given the complex pathologies observed in pediatric neurosurgery, QI projects could potentially improve patient care. Overall, there is a need to characterize the degree of QI opportunities, training, and initiatives within the field of pediatric neurosurgery. Herein the authors aimed to define the current QI landscape in pediatric neurosurgery. METHODS: A cross-sectional survey was sent to all members of the American Association of Neurological Surgeons/Congress of Neurological Surgeons Joint Section on Pediatric Neurological Surgery via email. The responses were anonymized. Questions addressed several relatable QI topics including 1) training and participation in QI; 2) QI infrastructure; 3) QI program incentives; and 4) general opinions on the National Surgical Quality Improvement Program (NSQIP) database, various QI topics, and QI productivity. RESULTS: Responses were received from 129 participants (20% response rate). Most respondents practiced in an academic setting (59.8%) and at a free-standing pediatric hospital (65.4%). Participation in QI projects was high (81.7%), but only 23.8% of respondents had formal QI training. Only 36.5% of respondents had institutional requirements for QI work; the majority of those were only required to participate as a project team member. Nearly half of the respondents did not receive incentives or institutional support for QI. The majority agreed ("strongly" and "somewhat") that a QI course would be beneficial (75.5%), that QI projects should be considered for publication in neurosurgery journals (88.1%), and that there is a need for national quality metrics (81.4%). Over 88% have an interest in seeing QI project presentations at the annual Pediatric Joint Section meeting. Only 26.3% believed that the NSQIP was a useful QI guide. Respondents suggested further study of the following QI topics: overall rates of infection and their prevention, hydrocephalus, standardized treatment algorithms for common disorders, team communication, pediatric neurosurgery-specific database, access to care, and interprofessional education. CONCLUSIONS: Areas of opportunity include specialty-specific QI education, tactics for obtaining support to build the QI infrastructure, increased visibility of QI work within pediatric neurosurgery, and a review of available registries to provide readily available data relevant to this specialty.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".