4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
Bibliographic record
Abstract
OBJECTIVES/GOALS: Designed sampling from databases (DSD) methods have been used to cross-check electronic medical records for errors, structure study design, and, we hypothesize, can be used to make data collection for surgical quality metrics more efficient, particularly within national databases. We plan to apply statistical and DSD methods to accomplish the following aims: 1. Identify the most important elements in managing post-operative pain 2. Identify the most informative procedure or population-based targets to focus collection of additional, labor-intense detail surrounding adequacy of pain control (i.e., Patient Reported Outcome Measures (PROMs)). METHODS/STUDY POPULATION: Our study population includes all children, ages 1-18 years, captured in the National Surgical Quality Improvement Project-Pediatric (NSQIP-P) from 2019 to 2021. We plan to apply statistical (regression modeling) and DSD methods to accomplish the aims listed above. RESULTS/ANTICIPATED RESULTS: For Aim 1, we expect to identify patient, procedure, and perioperative pain management practices that influence postoperative pain. For Aim 2, we will focus on outcomes such as PROMs that are challenging to obtain. By applying DSD methods, we will identify specific procedure and/or population-based cohorts to capture PROMs and decrease data collection burdens, while maintaining power, as the project is scaled nationally to all of NSQIP-P. DISCUSSION/SIGNIFICANCE OF IMPACT: Data from this study will inform expansion of NSQIP-P to collect novel outcomes of clinical and societal importance without prohibitively increasing data collection burden.
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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 | 0.223 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".