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Managing painful chronic wounds: the Wound Pain Management Model

2007· review· en· W2158311799 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Wound Journal · 2007
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineChronic painPsychosocialChronic woundFeelingQuality of life (healthcare)Venous leg ulcerPain managementDepression (economics)Physical therapyIntensive care medicineSurgeryWound healingNursingPsychiatry

Abstract

fetched live from OpenAlex

Chronic wound pain is not well understood and the literature is limited. Six of 10 patients venous leg ulcer experience pain with their ulcer, and similar trends are observed for other chronic wounds. Chronic wound pain can lead to depression and the feeling of constant tiredness. Pain related to the wound should be handled as one of the main priorities in chronic wound management together with addressing the cause. Management of pain in chronic wounds depends on proper assessment, reporting and documenting patient experiences of pain. Assessment should be based on six critical dimensions of the pain experience: location, duration, intensity, quality, onset and impact on activities of daily living. Holistic management must be based on a safe and effective mix of psychosocial approaches together with local and systemic pain management. It is no longer acceptable to ignore or inadequately document persistent wound pain and not to develop a treatment and monitoring strategy to improve the lives of persons with chronic wounds. Unless wound pain is optimally managed, patient suffering and costs to health care systems will increase.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.370
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it