A Case Report for Quality Assurance: Understanding the NSQIP Database and Preventing Error in Peer Review
Bibliographic record
Abstract
Background: This report assesses the accuracy and applicability of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) and MIDAS data to measure quality in a Gynecologic Oncology service.Method: A retrospective chart review (Canadian Task Force classification II-1) from a community hospital Quality Assurance committee's evaluation of a Gynecologic Oncology service, assessing all patients undergoing any surgery on the Gynecologic Oncology service from January, 2014 to September, 2017.Results: Surgical data was tabulated from operative notes and office charts.NSQIP data was provided by the hospital Quality Assurance Department's applications licensed from the American College of Surgeons.Proprietary MIDAS "Inpatient Takeback Rate" was provided by the hospital Quality Assurance Department.Conclusion: Hand-counting of hospital cases provided the most accurate and consistent results.NSQIP data provided variable accuracy in abstraction and coding but was limited to hysterectomy procedures.The MIDAS calculation was broadly inaccurate and should not be viewed as a quality indicator.Misinterpretation of quality data by a QA Department can adversely affect a surgeon's practice.To further increase the accuracy and utility of the NSQIP database for Oncologic Gynecologists, suggestions for specific queries for Gynecologic Oncology and Gynecology case abstractions are made.
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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 | 0.088 | 0.447 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".