Missed opportunities within healthcare for an earlier diagnosis of HIV
Bibliographic record
Abstract
Despite guidelines, many individuals are not routinely tested for HIV within healthcare settings. Our objective was to quantify and characterize preceding clinical encounters by newly-diagnosed persons living with HIV in southern Alberta, Canada. We discuss the clinical impact of missed HIV testing, and options for remediation. Clinical encounters prior to HIV diagnosis including the discharge diagnosis were collected between 1 April 2011 and 1 April 2016. We followed the HIV Indicator Diseases across Europe Study criteria to identify HIV Clinical Indicator Conditions (HCICs) present at clinical encounters. Patients accessing prior care were compared to those who had not previously accessed care. Of 393 individuals, 231 (58.7%) had ≥1 encounter prior to diagnosis; 224 (57%) of encounters occurred in outpatient clinics, 130 (33.1%) in emergency departments, and 39 (9.9%) in urgent care clinics. Approximately 25% (n = 57) of patients who engaged healthcare had ≥ 1 recognized HCIC but did not receive HIV testing. The most frequent HCICs were infection (n = 34; 60%) and hematological disorders (n = 12; 21%). The median CD4 cell count at HIV diagnosis for patients with an HCIC was 127 cells/mm 3 . In this population, three of five patients had accessed healthcare prior to diagnosis with one of four presenting with HCICs but were not offered HIV testing. Protocols beyond the current recommendations are urgently required to address missed HIV diagnostic opportunities who engaged healthcare.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".