Effects of seasonal changes in temperature and humidity on incidence of necrotizing soft tissue infections in Halifax, Canada, 2001-2015
Bibliographic record
Abstract
OBJECTIVES: To explore weather seasonal variation in Necrotizing soft tissue infections (NSTI) in Halifax, Nova Scotia, Canada could be attributed to changes in environmental factors of temperature and humidity specifically. METHODS: A retrospective chart review of NSTIs between 2001 and 2015. Regional temperature and humidity data were obtained from the Environment Canada Agency, Halifax, Canada. Chi-square was used for categorical variables and continuous data was used for correlation analyses. Logistic regression was performed to analyze mortality. Results: Of 170 NSTI patients identified, more presented from March to July, especially when the temperature was greater than 10ºC. Higher incidence per 100,000 persons correlated with increased monthly temperatures (p less than 0.01). Monthly NSTI incidence was inversely related to mean humidity (p=0.005). Causative organism was associated with mean weekly temperature (p less than 0.01) but not humidity (p=0.66). Low body mass index, higher American Society of Anesthesiologists class, long intensive care unit stay, and shorter overall hospital stay were associated with mortality. No correlation was identified between temperature and humidity and mortality. CONCLUSION: This study demonstrates a tendency toward more frequent cases of NSTI with warmer, but less humid weather, without effect on severity or mortality.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".