Managing Labour in Women with COVID-19
Bibliographic record
Abstract
Since first reported in December 2019 in Wuhan, China, COVID-19 caused by Severe Acute Respiratory Syndrome (SARS) Corona virus2 (SARS CoV-2) quickly spread to become a pandemic that has caused significant morbidity and mortality. The rapidity of the spread of the virus and the high mortality at the outset threatened to overwhelm health systems worldwide, and, indeed, this significantly impacted maternal health, especially since there was minimal experience to draw from. Experience with Covid 19 has grown exponentially as the unique needs of pregnant and labouring women with COVID-19 infection have become more evident. Managing COVID-19 parturients requires a multidisciplinary team consisting of anaesthesiologists, obstetricians, neonatologists, nursing staff, critical care staff, infectious disease and infection control experts. There should be a clear policy on triaging patients depending on the severity of their condition and the stage of labour. Those at high risk of respiratory failure should be managed in a tertiary referral centre with facilities for intensive care and assisted respiration. Staff and patients in delivery suites and operating rooms should be protected by enforcing infection protection principles such as offering dedicated rooms and theatres to SARS CoV-2 positive patients and using personal protective equipment. All hospital staff must be trained in infection control measures which should be updated regularly. Breastfeeding and care of the new-born must be part of the healthcare package offered to COVID-19 parturient mothers.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".