Executive summary of the consensus document of the Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC: GEPI, GeSIDA, GESITRA-IC, GEIRAS) on screening for imported infectious diseases in immunocompromised patients
Bibliographic record
Abstract
The increase in global mobility has led to a higher prevalence of imported diseases in immunocompromised patients, often asymptomatic but with the potential for reactivation or severe progression. This executive summary presents the key recommendations of the consensus document developed by the Imported Pathology Study Group (GEPI-SEIMC) in collaboration with GeSIDA, GESITRA and GEIRAS (SEIMC), targeting healthcare professionals who manage immunocompromised individuals. Based on a structured narrative review, the document proposes systematic screening strategies to detect imported infections during their asymptomatic phase, clearly distinguishing them from the clinical approach in symptomatic patients. Major infections of concern are summarized. Specific guidance is provided for people living with HIV, donors and transplant recipients, oncohematologic patients, and those receiving immunosuppressive therapy. Differences in reactivation risk, preferred diagnostic methods, and therapeutic decisions in cases of latent infection are highlighted. The document underscores the need to incorporate screening into pre-treatment and pre-transplant assessments, promoting standardized protocols.
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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 | 0.029 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".