Bibliographic record
Abstract
Reply Thank you for the opportunity to respond to the letter by Dr. Taylor. We agree with Dr. Taylor that follow-up data of infants who have received prophylaxis is important. As he points out, those infants who experience a respiratory syncytial virus (RSV)-positive hospitalization represent the failure rate of the product when the product is used in a real-life setting—a measure of effectiveness. That proportion is not necessarily expected to be the same as the values found in randomized controlled clinical trials, in which recruited patients and procedures are not always representative of the reference population. Thus, efficacy and effectiveness estimates may vary substantially. Dr. Taylor also expressed surprise that the calculated admission rate of 0.2% for RSV infections in infants with a gestational age (GA) of 33 to 35 completed weeks1 was not considered significantly different from that found in other groups (1.34% for <29 completed weeks and 1.25% for 29–32 completed weeks GA). In the CARESS study, a total of 588 infants were prophylaxed for prematurity and had GA of 33 to 35 completed weeks. Of those, 14 were hospitalized for any respiratory illness, 12 were tested for RSV, and only 1 was positive for an RSV infection (1/12 × 14/588 = 0.2%). While the proportion looks numerically lower, an estimate based on a single RSV-positive hospitalization gives a wide confidence interval around that estimate. As shown by a χ2 test, the 3 proportions were not statistically significantly different from each other (χ2 = 1.859, df = 2, P = 0.395). As can be seen in Table 1, the expected count in that cell is 5.3, which is not statistically different from the single RSV-positive case detected and the proportions in the other cells.Table 1: χ2 analysis of RSV-positive hospitalizations in premature infantsDr. Taylor compares this result with that of the Palivizumab Outcomes Registry, which found an overall RSV hospitalization rate of 0.8% in the 32 to 35 weeks' GA group.2 He states that the overall RSV hospitalization rate in this group is 1.1%; however, this is incorrect since the quoted percentage applies to infants who were >35 completed weeks' GA. However, there are several differences between the 2 studies that could attribute to the different rates of hospitalization for RSV infection. First, there is a large difference in the number of patients included in the calculation. As well, the 32 to 35 completed weeks' GA group in the Palivizumab Outcomes Registry included infants with other risk factors such as congenital airway anomalies, neuromuscular disease, and other underlying conditions that predisposed the infants to respiratory infection. The CARESS registry premature group did not include any infants with underlying medical illnesses; instead, the latter patients were classified by their specific underlying comorbidities. The differences in classification by the 2 studies make comparisons between the CARESS registry and the Palivizumab Outcomes Registry difficult. In summary, we would like to reassure Dr. Taylor and other readers that the calculated percentage of 0.2% is correct and represents the true rate of RSV positive hospitalization in the 33 to 35 weeks' GA prophylaxed group. We would also point out that the estimate is based on a single admission and that the true admission risk for those who were not prophylaxed in this population cannot be determined in the CARESS registry. Ian Mitchell, MB ChB, FCCP, FRCPC Bosco Paes, MB BS, FRCPI, FRCPC Abby Li, MSc Krista L. Lanctôt, PhD Sunnybrook Health Sciences Centre University of Toronto Toronto, Canada
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.271 | 0.130 |
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".