The Toronto Symptom Assessment System for Wounds
Bibliographic record
Abstract
OBJECTIVE: To formulate a patient-rated assessment tool that facilitates the measurement of pain and polysymptom distress directly related to all classes of wounds. DESIGN: A prospective observational study derived from a sequential case series of patients with advanced illness was carried out to determine the most common symptoms associated with wounds from 9 distinct classes (malignant, pressure ulcers, iatrogenic, traumatic, diabetic foot ulcers, venous ulcers, arterial ulcers, infections/inflammatory lesions, and ostomies). Ten wound-related symptoms were identified and used to create a patient-scored assessment tool. The Toronto Symptom Assessment System for Wounds (TSAS-W) was then developed and used in a pilot trial during which patients completed TSAS-W at baseline and 7 days later. PARTICIPANTS: Five hundred thirty-one patients either presented with wounds at baseline or developed them during the 24-month follow-up period. Patients affected by any type of wound were asked to report on the top 3 symptoms directly attributable to their wounds. The pilot trial of TSAS-W involved 103 wounds afflicting 83 sequential patients. MAIN RESULTS: The most prevalent wound-related symptoms included pain, exudation, odor, itching, bleeding, aesthetic concern, swelling, and mass and bulk effects from the wound and associated dressings; 78.6% of the TSAS-W assessments were carried out by the patient alone, 14.6% were carried out by the patient assisted by a caregiver, and 6.8% were carried out entirely by a caregiver. The summation of all 10 TSAS-W parameters, the global wound symptom distress score (GWSDS), resulted in a mean for all wounds of 34.47 at baseline and decreased to a mean of 28.40 at 7 days later. Cosmetic or aesthetic concern and/or distress was associated with the highest mean scores of all symptoms. Malignant wounds and wounds involving the perineum and genitalia were associated with the highest GWSDSs. CONCLUSION: The TSAS-W is a new tool for systematically assessing the degree of pain and polysymptom distress associated with all classes of wounds. It is modeled after the Edmonton Symptom Assessment System that is widely used and validated in the palliative care arena. TSAS-W is composed of 10 symptom parameters that are individually assessed on 11-point numeric rating scales (0-10). The summation of all of the element symptom scores equates to a GWSDS. It may be used in the clinical setting to guide wound-related pain and polysymptom management. In addition, TSAS-W may be useful as a tool in facilitating clinical audit and future wound care research.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 | 0.000 |
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 teacher head, 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".