The oncology impact of highly active antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of using highly active antiretroviral therapy (HAART) in a human immunodeficiency virus (HIV) infected population on the chemotherapy related costs of treating acquired immunodeficiency (AIDS)-related cancers. METHODS: We used the Southern Alberta Clinic (SAC) database to define the incidence and prevalence of AIDS-related cancers in a geographically defined HIV population in both the pre- HAART and HAART eras, and subsequently, the Alberta Cancer Board Pharmacy database to determine the chemotherapy associated costs of the cancer treatment. RESULTS: During both eras, 60% of AIDS-related cancer patients received chemotherapy. The absolute number of patients treated in the pre-HAART era was 70, but during the HAART era, due to the decreased incidence of these cancers, only 13 patients received chemotherapy. The number of distinct regimens used for AIDS cancer treatment standardised, and decreased from 29 to six between eras. The average per patient cost of chemotherapy in the pre-HAART era was $6111, while in the HAART era it rose to $8817. However, the cost avoidance in chemotherapy costs, due to HAART use in the 'at risk' population, averaged $123 439/year or $471 per 'at risk' patient. CONCLUSION: The introduction of HAART has dramatically reduced the amount spent on chemotherapy due to a decreased incidence of AIDSrelated cancers, even though the individual patient treatments have become more effective and expensive.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".