Demographic, Clinical and Microbiological Characteristics of Maternity Patients: A Canadian Clinical Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine the demographic, clinical and microbiological characteristics of a representative Canadian obstetrical population. DESIGN: A one-year cohort study of all maternity patients who were followed to delivery, using detailed patient questionnaires containing more than 60 demographic and clinical variables, and three microbiological evaluations during gestation - first trimester, 26 to 30 weeks, and labour and delivery. Outcome measurements included birth weight and gestational age. SETTING: Labour and delivery suites of all office obstetrical practices affiliated with a single hospital. POPULATION STUDIED: A consecutive sample of pregnant women in the study practices during one year were eligible for enrolment; 2237 consecutive patients were approached for consent, 2047 enrolled and 1811 completed the study through delivery. RESULTS: The average patient was white, married and 29 years of age. Slightly more than half of the patients had postsecondary education, but 10% fell below the national poverty line for income. Frequency of factors linked to adverse pregnancy outcomes included cigarette smoking (19%), alcohol ingestion (18%), previously having had a premature infant (7%), and maternal diabetes (2%). Overall prevalence of genital microbes variously implicated in prematurity was 37% for ureaplasma, 11% for group B streptococcus and 4% for Mycoplasma hominis. Prevalence of bacterial vaginosis was 14%. The median gestational age for the cohort was 39 weeks, with 7% of infants born less than 37 weeks' gestation. Mean birth weight was 3415 g. CONCLUSIONS: The present clinical cohort represents demographic and medical characteristics of the Canadian obstetrical population. The birth outcomes are consistent with national data. This database provides valuable information about a general obstetrical population that is managed by a universal health care system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".