Bibliographic record
Abstract
To the Editor—We thank Dr Skowronski and colleagues for their interest in our article. They raise a number of important points. In particular, they highlight the issue of missing frailty data, which we also discuss in our article. It is often the case that the people who are missing data that contribute to frailty measures are the ones who are most frail and vulnerable, thus highlighting some of the challenges of research involving frail older adults. This is not to say that we should give up on studying older adults or studying the impact of frailty, but rather to say that we should do the best we can with the data that can be obtained. As Neuzil and Chen stated in the editorial commentary accompanying our article, “As researchers, our challenge is to assess vaccine effectiveness using the best possible methodologies, and to discern differences in the performance of different vaccine formulations in specific populations” [1]. We heartily agree. Our network complements other networks such as the Canadian Sentinel Practitioner Surveillance Network led by Dr Skowronski [2], which make important contributions to the field. However, such outpatient networks underrepresent older adults, who experience the greatest burden of influenza illness, and do not collect detailed patient-level data, such as required for our frailty measure. Each approach provides a piece of the overall puzzle. Continued efforts are needed to understand vaccine effectiveness and the burden of influenza illness for older adults across the spectrum of frailty. The Serious Outcomes Surveillance (SOS Network) has been collecting data since the 2010–2011 influenza season. We are currently working on further studies of frailty and functional outcomes, including strain-specific analyses and pooling across seasons to increase sample size and power. These results will be forthcoming. Potential conflicts of interest. M. K. A. reports grant funding from GSK, Pfizer and Sanofi Pasteur. S. A. M. reports payments from the GSK group of companies, during the conduct of the study; and reports payments from Pfizer, Merck, Novartis and Sanofi, outside the submitted work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.078 | 0.046 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".