Who Stays and Who Goes: Predictors of Admission among Patients Presenting with Febrile Illness and a Positive Malaria Rapid Diagnostic Test in a Rural Ugandan Health Center
Bibliographic record
Abstract
Not much is known about clinical decision-making in rural, low-resource settings regarding fever, a common reason for presentation to care. In this prospective cohort study of patients presenting with febrile illness to a rural Ugandan health center, we examined demographic and clinical factors predictive of an initial disposition of inpatient admission after clinical evaluation, but before laboratory testing. We then assessed the association of laboratory results and system factors with a change between initial and final disposition plans. Four thousand nine hundred twenty-four patients with suspected febrile illness were included in the primary analysis. The strongest predictors for an initial disposition of admission after clinical examination were impaired consciousness (adjusted risk ratio [aRR], 3.21; 95% confidence interval [CI]: 2.44-4.21) and fever on examination (aRR, 2.27; 95% CI: 1.79-2.87). Providers initially planned to discharge patients with significant vital sign abnormalities, including tachypnea (3.6%) and hypotension (1.3%). Anemia strongly predicted a final disposition of admission after an initial disposition of discharge (aRR, 48.34; 95% CI: 24.22-96.49); other laboratory abnormalities, including hypoglycemia and acidosis, did not change disposition planning. In those with an initial disposition of admission, living farther than the two neighboring villages was associated with a final disposition of discharge (aRR, 2.12; 95% CI: 1.10-4.12). A concerning number of patients with abnormal vital signs and laboratory results were not admitted for inpatient care. Geographic factors may influence a patient's final disposition contrary to a provider's initial disposition plan. Future work should assess longer term outcomes after discharge and a broader study population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".