Indirect impact of the COVID-19 pandemic on emergency department presentations and hospital admissions for urgent early pregnancy conditions: a population-based retrospective cohort study
Bibliographic record
Abstract
Objective: To compare emergency department (ED) presentations and hospital admissions for urgent early pregnancy conditions in Victoria before and after the onset of COVID-19 lockdown on 31 March 2020. Design: Population-based retrospective cohort study Setting: Australian state of Victoria Population: Pregnant women presenting to emergency departments or admitted to hospital Methods: We obtained state-wide hospital separation data from the Victorian Emergency Minimum Dataset and the Victorian Admitted Episodes Dataset from January 1, 2018, to October 31, 2020. A linear prediction model based on the pre-COVID period was used to identify the impact of COVID restrictions. Main outcome measures: Monthly ED presentations for miscarriage and ectopic pregnancy, hospital admissions for termination of pregnancy, with subgroup analysis by region, socioeconomic status, disease acuity, hospital type. Results: There was an overall decline in monthly ED presentations and hospital admissions for early pregnancy conditions in metropolitan areas where lockdown restrictions were most stringent. Monthly ED presentations for miscarriage during the COVID period were consistently below predicted, with the nadir in April 2020 (790 observed vs 985 predicted, 95% CI 835-1135). Monthly admissions for termination of pregnancy were also below predicted throughout lockdown, with the nadir in August 2020 (893 observed vs 1116 predicted, 95% CI 905-1326). There was no increase in ED presentations for complications following abortion, ectopic or molar pregnancy during the COVID period. Conclusions: Fewer women in metropolitan Victoria utilized hospital-based care for early pregnancy conditions during the first seven months of the pandemic, without any observable increase in maternal morbidity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".