Underdetection of pre-existing HIV/AIDS during psychiatric hospitalizations
Bibliographic record
Abstract
OBJECTIVES: People with severe mental illness are 10 times more likely to have HIV/ AIDS than the general population, yet little is known about the characteristics and frequency of recognition of pre-existing HIV/AIDS diagnoses among inpatients with severe mental illness. This study examines documentation rates of pre-existing HIV/ AIDS among inpatients within psychiatric hospitals in New York State. DESIGN: Retrospective cohort study to examine recognition of pre-existing HIV/AIDS among psychiatric inpatients. METHODS: Patient-level Medicaid claims records were linked with hospital and regional data for people admitted to psychiatric inpatient units in New York State. Presence of HIV/AIDS diagnoses prior to psychiatric hospitalization was coded for each inpatient (n = 14 602). Adjusted odds ratios of undocumented HIV/AIDS diagnoses at the time of discharge were calculated using logistic regression analyses. RESULTS: About 5.1% (741/14 602) of unique psychiatric inpatients had pre-existing HIV/AIDS diagnoses. Of these inpatients, 58.3% (432/741) were not coded as having HIV/AIDS upon discharge. Higher rates of missed detection were associated with younger age, non-Hispanic white race/ethnicity, shorter length of stay, more distal coding of an HIV/AIDS diagnosis, and fewer HIV/AIDS-related Medicaid claims in the past year. Hospitals with higher readmission rates also had higher rates of undetected HIV/AIDS diagnoses. CONCLUSION: Over half of inpatients previously diagnosed with HIV/AIDS did not have their HIV-positive status noted upon discharge from psychiatric hospitalization. This finding underscores how frequently clinically significant medical comorbidities fail to be incorporated into psychiatric treatment and treatment planning. Inpatient clinicians are missing important opportunities to optimize HIV/AIDS treatment and reduce morbidity and mortality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".