Antenatal corticosteroids are currently used excessively and more stringent controls on their use should be established: <scp>AGAINST</scp>: Current use of antenatal corticosteroids effectively reduces neonatal morbidity and mortality
Bibliographic record
Abstract
What would happen if more stringent controls were imposed on the administration of antenatal corticosteroids (ACS)? The rate of eligible women receiving ACS would undoubtedly decline, undoing progress that has taken decades to achieve. Although Liggins and Howie (Pediatrics 1972;50:515–25) demonstrated that ACS significantly reduced neonatal respiratory distress syndrome in 1972, this intervention did not gain immediate widespread clinical acceptance. It was only after a 1994 recommendation from the National Institute of Child Health and Human Development to administer ACS in the setting of threatened preterm birth (NIH Consensus Statement 1994;12:1–24) that use of ACS became routine clinical practice. Despite the known benefits of ACS, including reduction of not only neonatal respiratory distress syndrome but also necrotising enterocolitis, intraventricular haemorrhage and overall mortality, the rate of administration remains suboptimal. A study from 2014 reviewed preterm deliveries from 2009 to 2011 at a tertiary care centre in the USA and found that before delivery, only 69.6% of eligible women received both doses of ACS (Chandrasekaran and Srinivas Am J Obstet Gynecol 2014;210:143.e1–7). In 2015, a study from Canada reviewed an administrative database of live births from 1988 to 2012 to assess rates of ACS administration and found an underuse of ACS at 33–34 weeks of gestation (Razaz et al. Obstet Gynecol 2015;125:288–96). Although current practice patterns may be trending towards improvement, this is not enough. Every neonate born before 34 weeks should receive a course of ACS. For many causes of preterm birth it is difficult to determine the best time to administer ACS. A study of a single tertiary-care centre in the USA reviewed preterm births from 2006 to 2011 and found that while 93% of eligible women received ACS, optimal timing occurred in only 40.4% of them (Levin et al. BJOG 2016;123:409–14). Specifically, optimal timing occurred in 36.3% of women with symptomatic preterm contractions, 46.2% of women with preterm premature rupture of membranes, 62.1% of women with hypertensive disorders of pregnancy and 20.6% of women with placental abruption/bleeding. Given that our ability to determine who will deliver preterm and when is poor, the choice is between administering steroids when there is concern for preterm birth—knowing that some women will receive ACS who do not need them—and withholding steroids until the probability of preterm birth is very high and increasing the proportion of preterm births where steroids are not administered. Given our current state of knowledge, it is unlikely that more stringent controls on ACS administration would provide any benefit. Ultimately what would improve care are better tools to predict preterm birth. Being better able to anticipate when women will deliver is the only way that the rate of proper steroid administration can be increased, while decreasing unnecessary administration. None declared. Completed disclosure of interests form available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".