Risk Assessment and Risk Distortion: Finding the Balance
Bibliographic record
Abstract
Pregnancy and birth have been conceptualized as medically problematic, with all pregnant women considered at risk and in need of medical monitoring. Universal application of risk scoring and surveillance as preemptive strategies in an effort to reduce risk is now standard obstetric practice. Labeling women "high risk" can result in more unnecessary interventions and have negative psychologic sequelae. When perceived pregnancy risk is out of proportion to the real risk, and when risk management procedures are applied to all women with benefit for only a few, the use of technology in caring for pregnant women becomes normalized. A learned reliance on technology can diminish women's own authoritative knowledge of pregnancy and birth. This may also have the unintended consequence of contributing to birth fear, a phenomena becoming more widely recognized. Health care provider-patient communication about pregnancy risk can be presented in a manner that encourages informed compliance rather than informed choice. Evidence-based risk assessment is essential to providing optimal prenatal care. Using tools such as the Paling Palette can help health care providers present balanced and readily understood information about risk.
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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 | 0.125 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.072 |
| Scholarly communication | 0.021 | 0.042 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".