936 Frostbite and Social Determinants of Health: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction Frostbite is a type of cold thermal injury caused by several mechanisms, including direct cellular injury and indirect injury from ischemia and reperfusion. While there has been considerable emphasis on the physiology and treatment of frostbite, there has been a lack of extensive research investigating the impact of Social Determinants of Health (SDoH) on frostbite injuries. SDoH are non-medical factors that impact health, such as income, housing, and childhood environment. These factors can influence health inequities and have been shown to impact health more than healthcare or lifestyle. Addressing this gap would provide us valuable insights for developing effective intervention and prevention initiatives. Methods We conducted a scoping review guided by the methodology framework of Arksey and O’Malley and the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols Extension for Scoping Reviews Guidelines (PRISMA-ScR). The MedLine database was searched to identify studies that met our review criteria. Studies of various designs and methodologies published since January 1, 2000 that examine SDoH in relation to frostbite injury in adults aged 18 or older were considered for inclusion in this review. Results The search identified 484 studies, 24 of which were retained in the final review. The majority of the manuscripts identified a SDoH with substance use disorder (n=15), older age (n=10), living with a mental disorder (n=12), experiencing homelessness (n=9) and male sex (n=19) being key determinants in the context of frostbite. Conclusions In conclusion, we have illustrated how social determinants of health such as substance use disorder, experiencing homelessness older age, male sex, and a psychiatric history can impact one’s risk of acquiring a frostbite injury. Furthermore, these factors should not be seen in isolation of each other but understood in the context of a Venn diagram, with each factor as a circle and with each overlap of the circles increasing the individual’s risk of harm exponentially. Applicability of Research to Practice Understanding predictors of frostbite injury provides the potential to facilitate early interventions and treatments, which may decrease severity of injury and guide public health programs. Interventions informed by this study can potentially modify the trajectory of future health and social outcomes in individuals. Funding for the Study Foundation funding
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".