Building Knowledge in Maternal and Infant Care
Bibliographic record
Abstract
Our ultimate goal as obstetric and neonatal care providers is to optimize care for mothers and their babies. As such, we need to identify practices that are associated with good outcomes. Although the randomized controlled trial is the gold standard for establishing the benefits of interventions, trials are very expensive and must be reserved for the most important of clinical questions. As an alternative, continuous quality improvement involves iterative cycles of practice change and audit of ongoing clinical care. An obvious prerequisite to this is ongoing data collection about interventions and outcomes, as well as demographics, pregnancy characteristics, and neonatal care that may affect the intervention- outcome relationship. In Canada (as in some other developed countries), much of the country is covered by regional reproductive care databases. These collect information on maternal demographics, pregnancy characteristics, labour and delivery, and basic information on maternal and perinatal outcomes. The primary objective of these databases is to monitor geographical trends and disparities in health outcomes. As such, there is little information about interventions, especially outside the period of labour and delivery. Also, there is no standardization of definitions, and efforts to produce a “minimal dataset” have not yet yielded agreement, even after many years of work. A more comprehensive system is required. Moving in this direction would serve many purposes: efficiency, economy in the setting of shrinking budgets, standardization of definitions, collaboration, and creation of stable background data collection onto which researchers could “clip” extra data required for specific studies. These activities would lay the foundation for the electronic health record, which cannot build its foundation on the “Tower of Babel” that is our current definitional structure in women’s health and obstetrics, in particular. Continuous quality improvement efforts and interaction with regional reproductive care programmes will facilitate translation and transfer of knowledge to care-givers and patients. These efforts raise concerns about privacy and security which remain major barriers to the EHR. However, security must be balanced with the need for health information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".