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Quality-of-Life and Outcome Predictors following Massive Burn Injury

2005· article· en· W2063336258 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2005
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineTotal body surface areaQuality of life (healthcare)PopulationBody surface areaBurn injuryMultivariate statisticsRehabilitationUnivariateBayesian multivariate linear regressionMultivariate analysisPhysical therapyEmergency medicineRegression analysisSurgeryInternal medicineStatisticsEnvironmental health

Abstract

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BACKGROUND: Quality of life is a major criterion when decisions regarding resuscitation, reconstruction, and rehabilitation of patients with massive burn injuries are being considered. There has been little research focusing on quality of life following burn injuries involving more than 50 percent total body surface area in the adult population. The authors' goals were to describe quality of life and identify specific clinical and functional indices that predict good quality of life following massive burn injuries. METHODS: Using a prospective study design, 47 patients who survived a massive burn between 1980 and 2001 were recruited from a single burn unit. Clinical data were collected from hospital records, function was assessed using the Abbreviated Burn-Specific Health Scale, and quality of life was assessed using the Short Form-36 survey. The Short Form-36 scores were compared with population norms. Univariate and multivariate regression analyses were used to identify factors predicting Short Form-36 scores. RESULTS: Overall, the mean age was 28 +/- 1.8 years, 96 percent (45 of 47) were men, and the mean burn size was 64 +/- 2.1 percent total body surface area. Compared with Canadian population norms, burn patients had significantly lower Short Form-36 scores in the domains of role physical (69.1 versus 82.1, p = 0.0067) and general health perception (67.2 versus 77.0, p = 0.00014). At the time of injury, the amount of total full-thickness burn predicted follow-up Short Form-36 physical summary scores (R2 = 15 percent, p < 0.001). At the time of follow-up, addition of the patient's hand function significantly contributed to the prediction of Short Form-36 physical summary scores (R2 = 44 percent, p < 0.001). At the time of injury, the age of the patient predicted follow-up Short Form-36 mental summary scores (R2 = 25 percent, p < 0.001). At the time of follow-up, addition of the patient's perceived level of social support significantly contributed to the prediction of Short Form-36 mental summary scores (R2 = 44 percent, p < 0.001). CONCLUSIONS: Survivors of massive burn injury reported a good quality of life in most Short Form-36 domains. The authors identified the size of the total full-thickness injury and the age of the patient as factors available at the time of injury that predict quality of life. The addition of hand function and the patient's perceived level of social support at the time of follow-up improved prediction of quality of life. Accordingly, this information on quality of life after massive burn injury could aid in decision making at the time of resuscitation, reconstruction, and rehabilitation.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.023
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.038
GPT teacher head0.305
Teacher spread0.267 · 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