Study of Association of Demographic Variables with Types of Burns Presenting in a Tertiary Care Medicolegal Clinic
Bibliographic record
Abstract
Background: In day to day life every human whether young or the old happens to deal or face the fire sources or any relevant entity. Great research is available in all the regions of the world and generous emphasis had been delivered widely. Though the advantages of the energy sources outweighs its disadvantages if dealt with proper care but accidents do happen in this process. The study focuses on the association of the demographic variables like age, gender, marital status and level of education. Aim: To observe the association of demographic variables with types of the burns. Methods: A total of 250 victims of burns presenting in the Medicolegal Clinic of King Edward Medical University Lahore/ Emergency of Mayo Hospital Lahore expanded over a period of several months from December 2017 to August 2018. Results: A grossly significant association of the age, gender, occupation and level of education of the victims of burns with types of different burns i.e. scalds, dry flame burns, chemical burns and electrical burns was observed with a 0.000 p value. A little lower significance of marital status of with a p value of 0.036 was observed when compared to different types of burns. Keywords: Scalds, Dry flame, Chemical, Electrical, Burns, Age, Gender, Marital Status, Occupation
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".