Hospitalization Outcomes in Pneumocystis Pneumonia Inpatient Population: A Comparison between HIV and Non-HIV Patients
Bibliographic record
Abstract
Objective To evaluate the difference in hospitalization outcomes, including morbidity and mortality among patients admitted for Pneumocystis pneumonia (PCP) with human immunodeficiency virus (HIV) and non-HIV condition. Methods A case-control study was done using the Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) data. We identified PCP and HIV as the primary and secondary diagnosis using ICD-9--CM diagnosis codes. We used the multinomial logistic regression model to generate odds ratios (OR). Results A total number of 1250 PCP patients were enrolled in this retrospective analysis. PCP patients with HIV had eight times higher odds of non-elective admission based on emergency condition (OR = 7.873, P < .001) compared to non-HIV patients. PCP patients with HIV had eight times higher odds of longer hospitalization of more than eight days (OR = 8.687, P < .001) compared to non-HIV patients. HIV patients with PCP had five times higher odds of severe morbidity or extreme loss of body function (OR = 5.277, P < .001). PCP patients with HIV had 22 times higher likelihood of in-hospital mortality (OR = 21.845, P < .001) compared to non-HIV patients. Conclusion PCP patients with HIV have a higher risk of severe morbidity and in-hospital mortality as compared to non-HIV patients. More attention needs to be paid to the elderly population that is at a higher risk of PCP with HIV. We need additional research and studies to direct the development of clinical care models for aiming early diagnosis and treatment of HIV in PCP patients.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".