Relative contributions of baseline patient characteristics and the choice of statistical methods to the variability of genotypic resistance scores: the example of didanosine
Bibliographic record
Abstract
BACKGROUND: Our aim was to investigate the respective role of statistical methodology and patients' baseline characteristics in the selection of mutations included in genotypic resistance scores. METHODS: As an example, the FORUM database on didanosine including 1453 patients was used. We split this population into four samples based on countries of enrolment (France n = 474, Italy n = 440, USA/Canada n = 219, others n = 320). We used both a continuous outcome measure (the viral load reduction at week 8) and a binary outcome measure (viral load decline at week 8 <0.6 log(10) or > or =0.6 log(10)) and both parametric and non-parametric methods for each outcome. RESULTS: Overall, 61 distinct mutations were selected by at least one method in at least one data set. The variability due to baseline characteristics varies from 79% to 88%, i.e. for a given method applied to the four data sets >80% of the mutations are selected only once. The variability induced by the methodology varies from 49% to 56%, i.e. for a given data set approximately 50% of the mutations are selected by at least two methods. CONCLUSIONS: Baseline patient characteristics contribute more than the choice of statistical method to the variability of the mutations to be included in the genotypic resistance scores.
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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 | 0.069 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".