GAMBARAN PENDERITA PARUT HIPERTROFIK AKIBAT LUKA BAKAR YANG DINILAI DENGAN VANCOUVER SCAR SCALE DI RSUD DR. SOETOMO SURABAYA PERIODE AGUSTUS – NOVEMBER 2017
Bibliographic record
Abstract
Background: Hypertrophic Scar is the result from a disrupted healing process of burn \nwound. There is not any exact general description and assessment with Vancouver \nScar Scales based on the hypertropic scars at Dr. Soetomo General Hospital Surabaya \nyet. Therefore, the purpose of this study to find out the general description of \nhypertrophic scar patients due to burns that has been scored using Vancouver Scar \nScale at Dr. Soetomo General Hospital Surabaya. \nMethod: Observational descriptive research had been performed to all burn patients \nwith hypertrophic scar after burns who came to the Plastic Surgery Department of Dr. \nSoetomo General Hospital surabaya since August until November 2017. We collected \ndemographic data (gender, age, domicile, causes and anatomy location area and \nassessed with VSS and skin color. \nResults: The patients were 13 patients with proportion of 46% male and 54% \nfemale. The patients ranged in age from 3 to 59 years old, with 46% patients in age \n18-40 years old. Regarding domicile of patients, 69% were from outside \nSurabaya, 31% from Surabaya.The most common causes of burns was Scald burns \n(46%). The most common anatomy location area is upper extremity (23%). Skin type \n4 has 69% and Type 5 with 31%. The value of VSS > 7 is 84% and VSS ≤ 7 is 16%. \nConclusion: Patients with hypertrophic scars who had a VSS > 7 more in male \npatients, at age 18-40, living outside Surabaya, mostly due to burn injury cause by \nscald, the most anatomical location was upper extremity and with fitzpatrick type skin \ncolor 4.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".