Bibliographic record
Abstract
Wounds are a serious healthcare issue with profound personal, clinical and economic implications.Using a working definition of compromised wounds, this study examines the prevalence of wounds by type and by healthcare setting using data from hospitals, home care, hospitalbased continuing care and long-term care facilities within fiscal year 2011-2012 in Canada.It also evaluates several risk factors associated with wounds, such as diabetes, circulatory disease and age.Compromised wounds were reported in almost 4% of in-patient acute hospitalizations and in more than 7% of home care clients, almost 10% of long-term care clients and almost 30% of hospital-based continuing care clients.Patients with diabetes were much more likely to have a compromised wound than were patients without the disease.W ounds are a serious healthcare issue with profound personal, clinical and economic implications.They can be excruciatingly painful and debilitating, and they can undermine function, mobility and quality of life.Chronic wounds in particular present unique healing challenges to those whose health is already compromised.The treatments, medications, interventions and dressings associated with wounds also represent a significant financial burden to the healthcare system.Most importantly, many wounds are avoidable with the provision of better healthcare services and a greater focus on prevention.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".