COVID-19 and Recurrent Pregnancy Loss Management: Trends in Clinical Care From a Tertiary Centre
Bibliographic record
Abstract
OBJECTIVES: To investigate the impact of the COVID-19 pandemic on the care received by patients with recurrent pregnancy loss (RPL) in British Columbia, Canada. To explore the differential impact of socioeconomic status on health care utilization outcomes during the COVID-19 pandemic for patients with RPL. METHODS: This is a retrospective cohort study of patients from an RPL clinic located within a tertiary referral centre. Patients were divided into 2 groups based on the date of their initial visit to the clinic: (1) pre-pandemic group (March 1, 2018-February 28, 2020) and (2) pandemic group (March 1, 2020-February 28, 2022). Data were sourced from the RPL Clinic Database and Population Data BC. Outcomes assessed included visit trends, immediate pandemic impact, and socioeconomic effects. RESULTS: Demographic and clinical characteristics were not significantly different between study groups, except for increased referral rates to fertility clinics by the RPL clinic during the COVID-19 pandemic (4.90% vs. 9.50%). The mean number of visits per patient was comparable between pre-pandemic (3.50 ± 2.00) and during the pandemic (3.40 ± 3.40). However, monthly initial visits were lower during the pandemic (12.50 ± 3.10) compared with pre-pandemic (14.40 ± 4.83). Telehealth was rare in the pre-pandemic period and increased dramatically during the pandemic, with virtual visits reaching up to 64% of total and 94% of initial visits. The pandemic's onset caused immediate drops in total (38.80%) and initial visits (51.70%). Health care utilization was higher among those with less material deprivation, whereas contrasting effects were observed in those with less social deprivation. CONCLUSIONS: The COVID-19 pandemic impacted the care received by patients with RPL within a tertiary care centre. There was a shift in how services were provided to patients, uniquely impacting specific populations within the community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".