Vasa Previa: A New Paradigm in Patient Hospitalization, Timing of Delivery and Corticosteroid Use [11A]
Bibliographic record
Abstract
INTRODUCTION: Vasa Previa occurs when fetally-derived umbilical cord vessels cross the internal os of the cervix. The aim of this study was to evaluate parameters that influence outpatient versus inpatient management in these cases. METHODS: Research Ethics Board approval was obtained for this study. Consecutive cases of vasa previa as diagnosed at Mount Sinai Hospital, Toronto from January 2008 to December 2017 were retrospectively reviewed. Cases were categorized into 3 arms for analysis: outpatients (OP), indicated hospitalized (IH) and non-indicated hospitalized (NIH). The NIH arm included asymptomatic patients admitted for surveillance, while the IH arm included symptomatic patients admitted for antepartum hemorrhage. RESULTS: 84 cases of vasa previa were diagnosed during the study period. The IH group was admitted a median of 22.5 days longer vs the NIH group up to delivery. The IH group had the highest frequency of unplanned caesareans at 46.2%, vs 7.1% and 11.1% of cases in the NIH and OP groups, respectively. Despite having an equal number of unplanned caesareans, the NIH group delivered a median 1.7 weeks earlier vs the OP group (P=.0040). NIH patients were also more than twice as likely to receive corticosteroid therapy inappropriately vs the OP group (57.1% vs 27.8%, P=.0359). A median (IQR) of 5 (3.5-7.6) weeks between corticosteroid administration and delivery existed among patients who received this therapy inappropriately. CONCLUSION: Our evidence suggests that physicians have difficulty predicting imminent preterm delivery for proper corticosteroid use in this population, but may be able to offer asymptomatic patients the option of outpatient management.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".