Association between change of health care providers and pregnancy exposure to FDA category C, D and X drugs
Bibliographic record
Abstract
BACKGROUND: Changing health care providers frequently breaks the continuity of care, which is associated with many health care problems. The purpose of this study was to examine the association between a change of health care providers and pregnancy exposure to FDA category C, D and X drugs. METHODS: A 50% random sample of women who gave a birth in Saskatchewan between January 1, 1997 and December 31, 2000 were chosen for this study. The association between the number of changes in health care providers and with pregnancy exposure to category C, D, and X drugs for those women with and without chronic diseases were evaluated using multiple logistical regression, with adjusted odds ratios (ORs) and its 95% confidence intervals (CIs) as the association measures. RESULTS: A total of 18 568 women were included in this study. Rates of FDA C, D, and X drug uses were 14.35%, 17.07%, 21.72%, and 31.14%, in women with no change of provider, 1-2 changes, 3-5 changes, and more than 5 changes of health care providers. An association between the number of changes of health care providers and pregnancy exposure to FDA C, D, and X drugs existed in women without chronic diseases but not in women with chronic disease. CONCLUSION: Change of health care providers is associated with pregnancy exposure to FDA category C, D and X drugs in women without chronic diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".