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Record W2758673020 · doi:10.1504/ijhfms.2017.10008128

Toward a quantified assessment of total body surface area from anthropometric measurements for patients with burn injuries

2017· article· en· W2758673020 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 Journal of Human Factors Modelling and Simulation · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversité de MontréalÉcole de Technologie SupérieureCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsTotal body surface areaBody surface areaAnthropometryBody surfaceSoftwareBurn injuryPoison controlMedicineComputer scienceEmergency medicineSurgeryMathematicsGeometryInternal medicine

Abstract

fetched live from OpenAlex

The amount of replacement fluid a burn patient requires to survive depends on the ratio (RBSA) of burned body surface area to the total body surface area (TBSA). The 2D methods used by clinicians are imprecise. In this paper, preliminary result of a proposed approach using anthropometric measurements and MakeHuman (MH) software to evaluate RBSA is presented. To assess RBSA accurately with a personalised 3D model of the burn patient, a first critical step is to find a limited set of measures for TBSA assessment. 20 anthropometric measurements were acquired virtually on 40 3D models generated with MH software. Using several multiple regression analyses, it was demonstrated that four to seven measures are sufficient to obtain an accurate TBSA. These preliminary results highlight the relevance of using software such as MH, to assess TBSA of patients with major burn injuries based on a limited set of measures.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.249
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.213
GPT teacher head0.463
Teacher spread0.250 · 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