Bibliographic record
Abstract
The authors respond: We thank Dr Chang and colleagues for the comments regarding the CPS statement “Preventing hospitalizations for respiratory syncytial virus infections”. The comments have provided us with the opportunity to clarify some issues and to correct wording. This does not, however, affect the essence of our original recommendations. Although the palivizumab product monograph recommends dosing every 28 to 30 days, some programs administer the first two doses 21 days apart. The rationale is that palivizumab levels are more likely to be sub-therapeutic 28 days after the first dose than 28 days after subsequent doses. However, studies have shown that most levels are still well above 25 mg/dL to 30 mg/dL (the level at which RSV titres decreased by a mean of 99% in cotton rats) 28 days following the first dose (1). As pointed out by Chang et al, one of the original post-marketing studies showed that 75% of breakthrough RSV hospitalizations in a single RSV season occurred between the first and second dose (2). Other potential reasons for a predominance of breakthrough infections during the first 28 days of prophylaxis would be: 1) in the year that was studied, RSV season may have started early; and 2) the average age of infants offered palivizumab increases as the RSV season proceeds while the risk of RSV hospitalization decreases with age (3). In other words, breakthrough RSV hospitalization rates during the period of prophylaxis would not be predicted to be random. In the second study quoted by Dr Chang, covering four RSV seasons, 31% of breakthrough RSV hospitalizations occurred between the first and second doses, and 25% between the second and third doses (4) for a difference of only 6%, demonstrating that there is not usually a major difference in the incidence of breakthrough infections between the first two doses versus the next two doses. It would be informative to compare the timing of RSV hospitalizations in children given palivizumab to a control group. Pending such data, there is no major disadvantage to administering the first two doses 21 days apart if programs wish to continue that practice.
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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 | 0.039 |
| 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 | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.048 | 0.033 |
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".