The Effects Of Dobbs On Cancer Care
Bibliographic record
Abstract
When the US Supreme Court overturned Roe v. Wade in 2022, the justices declared more than seven times that the decision would “return the issue of abortion to the people’s elected representatives.” Dobbs v. Jackson Women’s Health Organization suggested this would free the judiciary of a role in abortion policy debates, but quite the opposite has occurred. Overturning constitutional protection for access to abortion has unleashed confusion, chaos, and conflict across states with incompatible laws, between state and federal laws, and at the patient’s bedside. Dobbs did not remove abortion from the dockets: In the first quarter of 2024, the US Supreme Court will hear two new abortion-related cases, one involving Food and Drug Administration (FDA) regulation of mifepristone, the other regarding state law conflicts with the federal Emergency Medical Treatment and Labor Act (EMTALA), which has protected patients with medical emergencies since 1986. The increasing legal chaos after Dobbs has led not only to profound interstate and federal-state conflict, but also deeper fragmentation in US health care, greater health risks for patients living in abortion restrictive states, and shifts in how and where medicine is practiced. Medical care has become more challenging and precarious for people of reproductive age and their providers.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 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".