Bibliographic record
Abstract
OBJECTIVE: To emphasize preconception care of women with type 1 diabetes and the role of primary care physicians in evaluating and counseling them. QUALITY OF EVIDENCE: Substantial level II evidence indicates that tight glycemic control before conception and early in pregnancy reduces the rate of congenital malformations. Most evidence concerning maternal and fetal risks during pregnancy in patients with type 1 diabetes is level III or IV. Little is published on the role of family physicians in providing preconception counseling or care. MAIN MESSAGE: Preconception care is effective in improving glycemic control early in pregnancy and in reducing the rate of congenital malformations. Preconception evaluation of type 1 diabetic patients involves assessment of prepregnancy glycemic control and diabetic complications. Preconception counseling includes discussing the rate of transmission of diabetes, the effects of pregnancy on maternal and fetal complications, and the use of contraception until optimal glycemic control can be attained. CONCLUSION: Primary care physicians often have frequent and early contact with women of reproductive age; they are ideal candidates for providing type 1 diabetic women with preconception evaluation and counseling.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".