Causes of the first AIDS‐defining illness and subsequent survival before and after the advent of combined antiretroviral therapy<sup>*</sup>
Bibliographic record
Abstract
OBJECTIVES: To analyse the impact of combined antiretroviral treatment (cART) on survival with AIDS, according to the nature of the first AIDS-defining clinical illness (ADI); to examine trends in AIDS-defining causes (ADC) and non-AIDS-defining causes (non-ADC) of death. METHODS: From the French Hospital Database on HIV, we studied trends in the nature of the first ADI and subsequent survival in France during three calendar periods: the pre-cART period (1993-1995; 8027 patients), the early cART period (1998-2000; 3504 patients) and the late cART period (2001-2003; 2936 patients). RESULTS: The three most frequent initial ADIs were Pneumocystis carinii (jirovecii) pneumonia (PCP) (15.6%), oesophageal candidiasis (14.3%) and Kaposi's sarcoma (13.9%) in the pre-cART period. In the late cART period, the most frequent ADIs were tuberculosis (22.7%), PCP (19.1%) and oesophageal candidiasis (16.2%). The risk of death after a first ADI fell significantly after the arrival of cART. Lower declines were observed for progressive multifocal leukoencephalopathy, lymphoma and Mycobacterium avium complex infection. After an ADI, the 3-year risk of death from an ADC fell fivefold between the pre-cART and late cART periods (39%vs. 8%), and fell twofold for non-ADCs (17%vs. 9%). CONCLUSIONS: The relative frequencies of initial ADI have changed since the advent of cART. Tuberculosis is now the most frequent initial ADI in France; this is probably the result of the increasing proportion of migrants from sub-Saharan Africa. After a first ADI, cART has a major impact on ADCs and a smaller impact on deaths from other causes. The risk of death from AIDS and from other causes is now similar.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".