International opinion—The true cost of wounds for Canadians
Bibliographic record
Abstract
For many wound carers within Canada getting a handle on the costs associated with their management of chronic wounds is difficult, if not impossible.There are some published figures geographically, both national and provincial, but most of these are not "standardized" to permit comparison easily or directly.One consistent theme from several international research studies, however, 1-12 is that they relate the costs of wounds, extrapolated or otherwise, to the total geographic healthcare costs.This provides a percentage figure as a benchmark, an approach which Canada has used previously.13 A simple literature search shows both the paucity of data generally across Canada, in some provinces and also the outdatedness of the data, with most of it being published over a decade ago.Due to the difficulties of capturing such cost data, most of these studies caution their results as an underestimate of the costs involved, with Canada being no different to the others.A recent editorial in the International Wound Journal 14 introduced an approach to estimate the possible costs of wound care using freely available governmental health data, population statistics, and the research findings of many international groups.Using these statistics and a simple formula provides an estimate of the likely costs of wound care within both Canada and the provinces and territories of which it is comprised.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".