Using Diagnostic Codes to Screen for Intimate Partner Violence in Oregon Emergency Departments and Hospitals
Bibliographic record
Abstract
OBJECTIVES: Many of the 2.5 million Americans assaulted annually by intimate partners seek medical care. This project evaluated diagnostic codes indicative of intimate partner violence (IPV) in Oregon hospital and emergency department (ED) records to determine predictive value positive (PVP), sensitivity, and usefulness in routine surveillance. Statewide incidence of care for IPV was calculated and victims and episodes characterized. METHODS: The study was a review of medical records assigned > or = 1 diagnostic codes thought predictive of IPV. Sensitivity was estimated by comparing the number of confirmed victims identified with the number predicted by statewide telephone survey. Patients were aged > or = 12 years, treated in any of 58 EDs or hospitals in Oregon during 2000, and discharged with one of three primary or 12 provisional codes suggestive of IPV. Outcome measures were number of victims detected, PPV and sensitivity of codes for detection of IPV, and description of victims. RESULTS: Of 58 hospitals, 52 (90%) provided records. Case finding using primary codes identified 639 victims, 23% of all estimated female victims seen in EDs or hospitalized statewide. PVP was 94% (639/677). Provisional codes increased sensitivity (51%) but reduced PVP (50%). Highest incidence occurred in women aged 20-39 years, and those who were black. Hospitalizations were highest among women aged > or = 50 years, black people, or those with comorbid illness. CONCLUSIONS: Three diagnostic codes used for case finding detect approximately one-quarter of ED- and hospital-treated victims, complement surveys, and facilitate description of injured victims.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".