Cause‐specific mortality after diagnosis of cancer among HIV‐positive patients: A collaborative analysis of cohort studies
Bibliographic record
Abstract
People living with HIV (PLHIV) are more likely than the general population to develop AIDS-defining malignancies (ADMs) and several non-ADMs (NADMs). Information is lacking on survival outcomes and cause-specific mortality after cancer diagnosis among PLHIV. We investigated causes of death within 5 years of cancer diagnosis in PLHIV enrolled in European and North American HIV cohorts starting antiretroviral therapy (ART) 1996-2015, aged ≥16 years, and subsequently diagnosed with cancer. Cancers were grouped: ADMs, viral NADMs and nonviral NADMs. We calculated cause-specific mortality rates (MR) after diagnosis of specific cancers and compared 5-year survival with the UK and France general populations. Among 83,856 PLHIV there were 4,436 cancer diagnoses. Of 603 deaths after ADM diagnosis, 292 (48%) were due to an ADM. There were 467/847 (55%) and 74/189 (39%) deaths that were due to an NADM after nonviral and viral NADM diagnoses, respectively. MR were higher for diagnoses between 1996 and 2005 versus 2006-2015: ADMs 102 (95% CI 92-113) per 1,000 years versus 88 (78-100), viral NADMs 134 (106-169) versus 111 (93-133) and nonviral NADMs 264 (232-300) versus 226 (206-248). Estimated 5-year survival for PLHIV diagnosed with liver (29% [19-39%]), lung (18% [13-23%]) and cervical (75% [63-84%]) cancer was similar to general populations. Survival after Hodgkin's lymphoma diagnosis was lower in PLHIV (75% [67-81%]). Among ART-treated PLHIV diagnosed with cancer, MR and causes of death varied by cancer type, with mortality highest for liver and lung cancers. Deaths within 5 years of NADM diagnoses were more likely to be from cancer than AIDS.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".