An evaluation of the decision‐making process regarding amniocentesis following a screen‐positive maternal serum screen result
Bibliographic record
Abstract
OBJECTIVES: To identify the decision-making factors and personal characteristics of women who opt for and against amniocentesis following a screen-positive maternal serum screen (MSS) result. METHODS: A questionnaire was mailed to 597 women who were randomly selected among women in the province of British Columbia (BC) who screened positive for Down syndrome (DS) on the MSS between January and June 2005. Subjects were evenly distributed across two main parameters: screen-positive women who opted for, and declined, amniocentesis (Groups 1 and 2, respectively). RESULTS: Significant differences (P < 0.05) between Groups 1 and 2 include; reasons for wanting the MSS, post-positive MSS anxiety level, risk of miscarriage associated with amniocentesis, MSS risk estimate, reasons for wanting, or not wanting amniocentesis, normal fetal ultrasound, attitudes towards termination and religious beliefs. About half of all women across both groups did not find the MSS helpful in their pregnancy, primarily stating that it caused unnecessary increased anxiety. CONCLUSIONS: To help avoid, or at least prepare women for the likelihood of increased anxiety following a screen-positive MSS result, and help prepare them for decision making, it is important to target MSS counselling to the individuality of the patient, and address these factors before MSS is undertaken.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".