The Coding Causes of Death in HIV (CoDe) Project
Bibliographic record
Abstract
BACKGROUND: The Coding Causes of Death in HIV (CoDe) Project aims to deliver a standardized method for coding the underlying cause of death in HIV-positive persons, suitable for clinical trials and epidemiologic studies. METHODS: The project incorporates detailed data collection, a classification system, and a centralized adjudication process performed by 2 independent reviewers. The methodology was tested in the Data Collection on Adverse events of Anti-HIV Drugs Study , and independent reviews of causes of death were compared. Logistic regression models identified factors associated with initial agreement by reviewers on underlying cause of death. RESULTS: A total of 491 reported fatal cases were adjudicated; in only 5% of cases the cause of death remained undetermined after adjudication. Reviewers initially agreed on the underlying cause for 339 (69%) deaths. As compared with deaths due to AIDS-related causes, the odds of agreement were more than 80% lower when deaths were ultimately deemed to be due to non-AIDS-related causes (odds ratio = 0.17 [95% confidence interval = 0.08-0.37]) or undetermined causes (0.11 [0.04-0.36]). The odds of initial agreement were also lower for deaths occurring in subjects with hypertension (0.43 [0.22-0.85]) and depression (0.43 [0.23-0.80]). CONCLUSIONS: The extent and format of data collected in the CoDe Project appear to be sufficient for an informed review, and the proposed coding scheme is adequate for obtaining an underlying cause of death.
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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 | 0.265 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".