An Advanced Nursing Directive for Children With Suspected Appendicitis: Protocol for a Quality Improvement Feasibility Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Pediatric appendicitis accounts for an estimated 7% to 10% of abdominal pain cases in the emergency department (ED). The diagnosis is time-consuming, and the investigative process depends on physician assessment, resulting in delays in diagnosis and therapeutic management. The utility of an advanced nursing directive (AND) to expedite this process is unclear and needs further exploration. OBJECTIVE This study aims to describe key components of ED flow in patients with suspected appendicitis seen at a pediatric ED and pilot a directive that allows ED nurses to perform an order set that includes blood work, urine tests, analgesics, fluids, and an abdominal-pelvis ultrasound prior to physician assessment. METHODS This study involves conducting a retrospective chart review alongside a quality improvement initiative to compare key ED flow metrics before and after AND implementation. Primary outcome measures include median time from ED triage assessment to ultrasound completion, analgesia administration, blood work results, and time to disposition (consult or discharge), alongside other key ED flow metrics for suspected appendicitis. Secondary outcomes will involve patient and caretaker satisfaction surveys. Descriptive statistics will be used to summarize the data. For differences in proportions, a chi-square test will be used. The Student t test will be used for continuous variables. A variable-controlled run chart will be performed to assess impact on ED flow metrics. Patient and family satisfaction surveys are administered immediately after the directive encounter and 7 days afterward. RESULTS There are currently 3900 patients who have been screened, 344 patients who have been enrolled, and 90 patients who have received the medical directive since implementation in June 2020. Interim results on reduction of time to diagnostic and therapeutic ED flow parameters and satisfaction surveys are expected to be published in February 2022. The final study endpoint will be in June 2022. CONCLUSIONS This study proposes a novel protocol for improving the diagnosis and treatment of suspected pediatric appendicitis through implementation of an evidence-based AND. This model may provide a standardized, international pathway for management of common pediatric and adult emergencies. INTERNATIONAL REGISTERED REPORT RR1-10.2196/33158
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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 | 0.085 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".