Bibliographic record
Abstract
Preterm birth, the leading cause of neonatal morbidity and mortality worldwide, is a major public health problem in terms of loss of life, long term disability (e.g. cerebral palsy, chronic lung disease), and health‐care costs. The outcome of preterm infants is directly related to the gestational age at delivery. The goal of tocolytic therapy is to reduce neonatal morbidity and mortality by delaying delivery, to allow for the administration of corticosteroids and/or the safe transfer to a tertiary‐care centre. However, currently available tocolytics in Canada (ritodrine, indomethacin, calcium antagonists, magnesium sulphate) have poor efficacy, have not been shown to increase the completion of a course of corticosteroids, are potentially associated with significant maternal/fetal side effects, and most importantly, have not been shown to improve neonatal outcomes. At the Canadian Tocolysis Consensus Conference, there was general agreement that recommendations should be based on good quality research evidence, particularly that of randomised clinical trials when available. It was concluded that there is little evidence to support the use of any of the currently available tocolytics; tocolytic use has not been associated with improved perinatal outcomes and often have detrimental effects on the mother. Therefore, questionable efficacy and potentially serious side effects may outweigh their use. Any new tocolytic demonstrated to improve neonatal outcome will have an immediate impact on societal and long term public health‐care costs.
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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 | 0.065 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".