Wounds in advanced illness: a prevalence and incidence study based on a prospective case series
Bibliographic record
Abstract
A prospective observational sequential case series was studied in order to ascertain an accurate inventory of the various wound types, their point prevalence and incidence rates and their anatomic locations in patients with advanced illness. Five hundred and ninety-three patients were serially assessed until their deaths. Forty-three individual wound types were identified and grouped into nine distinct classes. Data were stratified between patients suffering from malignant and non malignant disorders. One thousand and thirty-six individual wounds (average 1.8 wounds per patient) were identified at baseline. Eight hundred and ninety-one individual wounds (average 1.5 wounds per patient) were identified between baseline and their date of death. Pressure ulcers constituted the most commonly occurring wound class affecting more than 50% of all patients. Malignant wounds were observed only in cancer patients. Baseline point prevalence for pressure ulcers, traumatic wounds, venous ulcers and arterial ulcers in non cancer patients exceeded that in cancer patients. At baseline, iatrogenic wounds were more prevalent in cancer patients than in non cancer patients. Incidence rates for pressure ulcers, traumatic wounds, diabetic ulcers, arterial ulcers and ostomies in non cancer patients exceeded those in cancer patients. The broad range of wounds along with high rates of prevalence and incidence, identified in this study, reflects that wounds represent a significant management issue for patients with advanced illness. Therefore, there exists a need for advancement in modalities and measures aimed at risk assessment, prevention and appropriate goal-oriented management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".