Threatened Preterm Labor Admissions: A Population Based Study to Assess Trends and Financial Effect [35K]
Bibliographic record
Abstract
INTRODUCTION: Threatened preterm labor (TPTL) is a common hospitalization indication among pregnant women, often for unnecessarily prolonged periods of time. The accurate identification of women not actually in preterm labor avoids unnecessary and costly interventions. The purpose of our study is to describe admissions for TPTL and assess their financial burden. METHODS: We carried out a cohort study on all pregnancy admissions using the United States’ Healthcare Cost and Utilization Project-Nationwide Inpatient Sample database between 1999-2015. We identified all TPTL admissions and a comparison was done between length of hospital admission for patients who delivered during that same admission ("Delivery" group) versus those who were discharged without delivering ("Observation" group). RESULTS: There were a total of 15,337,934 pregnancy admissions during our study period, of which 1,307,934 were for TPTL. The rate of total TPTL admissions decreased over the 16-year period, with a mutual increase in rate of "delivery" and decrease in rate of "observation" admissions over time. Of the total TPTL admissions, 70.5% and 29.5% were in the "delivery" and "observation" groups, respectively. Within 48 hours of admission, 82% of the "delivery" group delivered, whereas 60.8% of the "observation" group were discharged. Of the remaining total- 52.2% delivered eventually, with more than half of them after more than 6 days of admission. In subset analyses, total cost for the "observation" admissions group was 4,059,004,590$ with mean cost of 10,782$ per patient per admission. CONCLUSION: Despite improvement in rate of total TPTL admissions throughout the years, and more specifically decline in rates of unnecessary admissions, given their high cost, consideration should be taken for discharging more patients no later than 48 hours, after which our clinical ability to discriminate those who will deliver is sparse.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".