Reply
Bibliographic record
Abstract
SIR—We gratefully acknowledge the interest that our article [1] has generated. The optimal treatment for patients who have virologic failure but immunologic improvement in response to therapy (i.e., those who have a “discordant response”) is a critical issue faced by clinicians who treat patients infected with HIV. As we suggested in our report, perhaps the one management principle that may be agreeable to all is to institute new therapies that are likely to achieve resuppression of the virus. How to manage such patients when these therapies do not exist remains controversial [1]. In our report, we analyzed the immunologic effects of therapy cessation on patients who had a discordant response [1]. These patients were nonselected and had their therapy discontinued for clinical reasons. In all patients, there was a dramatic decrease in the CD4 T cell count after cessation of therapy—a finding that prompted our suggestion that patients continue to receive the therapy associated with immunologic improvement to maximize the clinical benefit of the discordant response. Ideally, such an analysis should be performed in a randomized clinical study. The results of such a study were, in fact, recently published after our own report was submitted for publication. Using a 2:1 ratio, Deeks et al. [2] randomized 16 patients with a discordant response to either discontinue or continue therapy; they also analyzed an additional 7 patients who had a discordant response and who stopped receiving therapy. In their study, discontinuation of therapy for 12 weeks resulted in a decrease of 128 CD4 T cells/mm3, whereas continuation of therapy resulted in a decrease of only 12 CD4 T cells/mm3 (P = .005). Thus, in both our study [1] and that of Deeks et al. [2], cessation of therapy was associated with negative immunologic consequences.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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".