Barriers to health care and protocol-based treatment of ectopic pregnancy.
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether treatment provided for ectopic pregnancies was different for patients with identified barriers to health care, including ethnicity, lack of insurance, distance from the treating facility that provides services and undocumented residency, at an institution that utilizes a protocol-based algorithm for treatment of ectopic pregnancies. STUDY DESIGN: Charts of 401 patients who were diagnosed with ectopic pregnancy from January 1, 1993, through December 31, 1998, were reviewed to compare the use of medical treatment using methotrexate versus surgical treatment. Data were analyzed with respect to patient ethnicity, socioeconomic status (including insurance status and possession of a social security card [a proxy for legal residency status]), residence inside or outside the county of the treating facility, patient presentation and treatment outcomes. RESULTS: There was no difference in treatment modality or success of primary treatment for ectopic pregnancies between groups regardless of ethnicity, health care insurance, residence outside the county the treating facility was located in or possession of a social security number. CONCLUSION: The treatment of ectopic pregnancies at the University of New Mexico Health Sciences Center is consistent across ethnic and socioeconomic populations. A well-designed treatment protocol may help provide evidenced-based, consistent treatment for patients requiring care who also have identified barriers to medical and surgical treatment.
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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 | 0.012 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".