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Abstract PO-110: Determining the validity of patient pain markings for wound pain prior to debridement

2020· article· en· W3110342190 on OpenAlexaboutno aff
Jessi Noel, Rishabh Garg, Yingwei Yao, Michael T. Weaver, Debra Lyon, Joyce Stechmiller, Diana J. Wilkie

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2020
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineContext (archaeology)Physical therapyPain assessmentBody surface areaDescriptive statisticsPain managementSurgery

Abstract

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Abstract Introduction: Little research has been done to determine if the wound pain markings correlate with the changes in measured wound size. For cancer patients, wound pain could be caused by the tumor or cancer treatments. The study purpose was to evaluate the validity of patient pain markings (body surface area) as an indicator of wound size and changes in size over time. This study will contribute to the clinicians’ understanding of the patient reported pain outcomes as an indicator of wound healing. Methodology: Among a sample of 40 participants, 22 (55%) were female, 38 (95%) were non-Hispanic, and 35 (88%) were White and 5 (12%) were Black. Mean age of participants was 70.8 (SD=9.1) years, 13 participants completed high school, 16 completed some college, and 11 completed more than college. The majority of the participants (n=23, 58%) were married. All subjects were asked to complete the PAINReportIt, an electronic adaptation of the McGill Pain Questionnaire. Their wound areas were measured using the Silhouette device, which measured the wound length, depth, and volume. Using the subject’s PAINReportIt pain markings on a body outline, the ImageJ software algorithm calculated the body surface area (BSA) in pixels as marked for each pain site. Analyses included descriptive statistics and Kendall correlations. Results: From the 40 unique patients, there were 157 visits where both BSA and wound size data were recorded: 18 patients had 5 visits, 4 had 8 visits, 7 had 3 visits, and 7 had data from 2 visits. As a context to the magnitude of pain the patients reported at visit 1, the mean pain intensity was 2.9±2.7 (0-10 scale). The wounds were in the lower extremity for 39 patients and in the foot for 1 patient. The mean wound area was 2634±8005 mm2. 35 patients reported pain in 1 site, 1 reported 2 sites, 1 reported 3 sites, 2 patients reported pain in 4 sites, and 1 patient did not report pain sites at visit 1 but did so at other visits. The between- subject correlation between the wound size and the BSA area marked was tau=.33 (p<.01). The within-subject correlation between these two measurements was tau=.08 (p=.45). Conclusions: The findings show that the within-subject correlation between repeated BSA and wound area measurements was low, indicating that the BSA is not a good indicator of change in wound area over time. In the future, studies of a larger sample size are needed to confirm the degree of correlation between BSA and wound area. Citation Format: Jessi Noel, Rishabh Garg, Yingwei Yao, Michael Weaver, Debra Lyon, Joyce Stechmiller, Diana J. Wilkie. Determining the validity of patient pain markings for wound pain prior to debridement [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-110.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.090
GPT teacher head0.363
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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