Bibliographic record
Abstract
Over the past 30 years as caregivers, clinicians have been exposed to a plethora of new advanced wound dressings. The moist wound care revolution began in the 1970s with the introduction of film and hydrocolloid dressings, and today these are the traditional types of dressings of the advanced dressing categories. Wound-healing science has progressed significantly over the same period, as a result of intense clinical and scientific research around these product introductions. Today, the clinician understands moist wound healing, occlusion, cost effectiveness, wound bed preparation and MMP activity to name but a few of the many concepts in wound care that have flourished as a result of technology and product advancement. This review article presents a condensed history of dressing development over the past 30 years. However, in addition, such advancement is discussed in respect to its adoption in different parts of the world. The largest single markets of the world are generally the United States of America and Europe; as such, the development of both practice and technology generally begins there. Much has been written about these markets in previous review articles. For the purposes of this review, the development of wound care and the maturing of practice is discussed in respect to Canada, Japan and Australia representing smaller geographical areas where the development has been more recent but nonetheless significant.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".