Bibliographic record
Abstract
Young children readily transmit and acquire nosocomial infections. Children are also vulnerable to endogenous infections as a result of the breakdown of their normal defences by disease, invasive procedures or therapy. The increasing acuity of illness in hospitalized children and therapeutic advances have resulted in a patient population that is increasingly at higher risk for nosocomial infections. Antibiotic resistance has emerged as a problem in some paediatric hospitals, usually in intensive care and oncology units. Infection rates are the highest in neonatal and paediatric intensive care units (where bloodstream infections are the most frequent), and are usually associated with intravascular devices. On general paediatric wards, respiratory and gastrointestinal infections predominate, reflecting the occurrence in the community. The surveillance of nosocomial infections identifies priorities for infection control activities and permits evaluation of interventions.The prevention of transmission between patients and to personnel requires that certain measures be taken with all patients, and that additional precautions be taken with some infections, based on the route of transmission. The prevention of transmission from personnel involves ensuring that personnel are appropriately immunized and counselled about working with infections. The prevention of nosocomial infection also involves control of visitors, appropriate management of invasive procedures and devices, sterilization and disinfection of equipment, provision of a clean environment and adequate staffing. Severely immunocompromised children require extra protection, including ventilation systems that reduce the risk of exposure to filamentous fungi. Infection control in paediatrics is an evolving field that must adapt to changes in the paediatric patient population and in health care technology.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.027 |
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".