Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effective management of nonhealing wounds is based on a complete patient history, a detailed initial assessment of the wound, and an analysis of probable causative factors. This information is used to individualize a management strategy to the underlying pathophysiology preventing healing and to implement appropriate wound interventions. Regular reassessment of progress toward healing and appropriate modification of the intervention are also necessary. Accurate and clinically relevant wound assessment is an important clinical tool, but this process remains a substantial challenge. Wound assessment terminology is nonuniform, many questions surrounding wound assessment remain unanswered, agreement has yet to be reached on the key wound parameters to measure in clinical practice, and the accuracy and reliability of available wound assessment techniques vary. This article, which resulted from a meeting of wound healing experts in June 2003, reviews clinically useful wound measurement approaches, provides an overview of the principles and practice of chronic wound assessment geared to a clinical audience, and introduces a simple mnemonic, MEASURE. MEASURE encapsulates key wound parameters that should be addressed in the assessment and management of chronic wounds: Measure (length, width, depth, and area), Exudate (quantity and quality), Appearance (wound bed, including tissue type and amount), Suffering (pain type and level), Undermining (presence or absence), Reevaluate (monitoring of all parameters regularly), and Edge (condition of edge and surrounding skin). This article also provides some preliminary recommendations targeted to developing best practice guidelines for wound assessment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it