The situation of infection in the elderly in Spain: a multidisciplinary opinion document
Bibliographic record
Abstract
Infection in the elderly is a huge issue whose treatment usually has partial and specific approaches. It is, moreover, one of the areas where intervention can have the most success in improving the quality of life of older patients. In an attempt to give the widest possible focus to this issue, the Health Sciences Foundation has convened experts from different areas to produce this position paper on Infection in the Elderly, so as to compare the opinions of expert doctors and nurses, pharmacists, journalists, representatives of elderly associations and concluding with the ethical aspects raised by the issue. The format is that of discussion of a series of pre-formulated questions that were discussed by all those present. We begin by discussing the concept of the elderly, the reasons for their predisposition to infection, the most frequent infections and their causes, and the workload and economic burden they place on society. We also considered whether we had the data to estimate the proportion of these infections that could be reduced by specific programmes, including vaccination programmes. In this context, the limited presence of this issue in the media, the position of scientific societies and patient associations on the issue and the ethical aspects raised by all this were discussed.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".