Incidence of Human Immunodeficiency Virus Type 1 Dual Infections in Amsterdam, The Netherlands, during 2003–2007
Bibliographic record
Abstract
BACKGROUND: The occurrence of human immunodeficiency virus type 1 (HIV-1) dual infections in Amsterdam, The Netherlands, was examined during 2003-2007 to investigate whether the number of HIV-1 dual infections increased as the number of HIV-1 infected individuals increased during the same period. METHODS: All first HIV-1 genotyping sequences obtained from 2003 through 2007 were retrieved and examined for the number of degenerate base codes in the reverse-transcriptase fragment. A total of 72 patients had >or=34 degenerate base codes; for these patients, a fragment of the V3-V4 region of the env gene was amplified, cloned, and sequenced to verify the presence of an HIV-1 dual infection. The number of dual infections were counted for each year investigated. RESULTS: No significant change in the incidence of dual infections was observed in our population of patients, who were selected on the basis of the number of degenerate base codes in each patient's first HIV-1 sequence obtained from 2003 through 2007. The frequency of HIV-1 dual infections varied between 1.0% and 2.4% each year, with no significant trend over time (P = .49). Patients with HIV-1 dual infections were similar to patients with single HIV-1 infections in The Netherlands with regard to distribution of risk group, sex, and HIV subtype. CONCLUSION: The proportion of HIV-1 dual infections in The Netherlands did not increase from 2003 through 2007, although the HIV-1-infected population expanded in this period.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".