Different Strategies for Functionalising Nonwovens for Medical Use
Bibliographic record
Abstract
The global megatrends of an ageing population and the growing population, are driving the sustained expansion of healthcare. This is a rapidly growing and constantly evolving discipline that presents a multitude of opportunities that are being addressed by innovations in nonwovens for the diagnosis, treatment and prevention of disease, illness and injury. Nonwovens and other fibrous materials are extensively utilised in the healthcare sector including products from surgical wipes for infection control, to complex implantable grafts. Rapidly evolving clinical demands and new advancements in materials technology means there is a continuous need for nonwovens innovation and functionalisation. Areas of application for healthcare materials in current markets include implantable materials/tissue engineering (artificial ligaments etc), non-implantable materials (wound dressings, hygiene products, ostomy pouches), healthcare environment materials (surgical gowns, materials to reduce healthcare-associated infection (HCAI)), as well as assistive and therapeutic technologies. Professor Goswami will discuss the importance of functionalisation of nonwovens with case-studies from his research centre
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".