Development and Validation of the Stanford Obstetric Recovery Checklist (STORK)
Bibliographic record
Abstract
Background: Existing patient-reported outcome measures (PROMs) evaluating outpatient postpartum recovery lack content validity and were mostly not designed for this population. A Delphi process was performed, aiming to develop a patient-reported outcome measure for outpatient postpartum recovery and then evaluate it in a multicenter cohort study. Methods: Development of the Stanford Obstetric Recovery Checklist (STORK) involved 3 phases: (1) postpartum recovery questions were identified in published reviews; (2) after institutional review board approval, 16 multidisciplinary experts and patient stakeholders participated in 3 Delphi rounds (January 11 to April 12, 2021) to select items, resulting in the development of STORK (47 items; total score range, 0-188, with 0 indicating the worst recovery and 188 indicating the best recovery); and (3) cognitive debriefing interviews were conducted with 10 postpartum individuals to finalize STORK items. Individuals then completed STORK during their inpatient stay and at 2, 6, and 12 weeks post partum in a prospective, 3-center, US longitudinal cohort study conducted from June 13, 2022, to February 28, 2023. Recruitment occurred until 300 six-week STORK surveys were completed. STORK was evaluated at 6 weeks for validity (ability to measure recovery), reliability, and responsiveness. Validity included (1) structural validity (exploratory factor analysis using root mean square residual [RMSR]; <0.08 indicates a good fit); (2) convergent validity (correlation with global health visual analog scale score [GHVAS; scale, 0-100] and EuroQoL Five-Dimensions Three-Levels [EQ-5D-3L]); (3) discriminant validity (mean difference in STORK scores with GHVAS <70 vs ≥70); and (4) confirmatory telephone interviews with postpartum individuals scoring the highest and lowest 10th percentiles of STORK scores. Reliability (consistency of STORK scores) was evaluated using Cronbach α, interitem correlation, split-half reliability, and floor and ceiling effects. Responsiveness (ability of STORK to detect changes in recovery over time) was evaluated using percentage change in score from baseline to 12 weeks. Results: A total of 525 individuals were recruited after all delivery modes (response rate, 62% [324 of 525] at 6 weeks); 498 (mean [SD] age, 33.3 [4.9] years) completed baseline inpatient postpartum surveys. STORK demonstrated validity: (1) a 4-factor model was the best fit (RMSR = 0.05); (2) correlation with GHVAS scores was ρ = 0.52 (95% CI, 0.43-0.61), and correlation with EQ-5D-3L scores was ρ = -0.67 (95% CI, -0.76 to -0.63); (3) STORK was able to discriminate between patients reporting good and poor recovery (good recovery: median STORK score, 151 [IQR, 136-163] vs poor recovery: median STORK score, 129 [IQR, 107-148]; P < .001); and (4) the highest and lowest scores corresponded to subjective assessments. STORK demonstrated reliability (Cronbach α = 0.92; interitem correlation r = 0.20; and split-half reliability ρ = 0.98). It also demonstrated responsiveness: percentage increases in overall STORK scores from baseline to week 12 were 19% after spontaneous vaginal delivery, 31% after operative vaginal delivery, 27% after scheduled cesarean delivery, and 20% after nonscheduled cesarean delivery (P < .001). Conclusion: In this cohort study of US individuals, STORK was found to be a valid, reliable, and responsive measure of outpatient postpartum recovery. Future clinical trials are needed to determine its clinical utility.
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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 | 0.057 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".