Mental Disorders in Necrotizing Fasciitis Compared to Matched Controls: A Longitudinal Population-Based Study
Bibliographic record
Abstract
Necrotizing fasciitis (NF) is associated with extensive surgery, amputations, and prolonged hospitalization that may increase stress on a patient and have deleterious consequences. What is not known is whether or not NF results in an increased incidence of mental disorders and associated healthcare utilization in the years after the infection compared to before infection. We hypothesize that the incidence of mental health disorders and associated health care utilization due to these outcomes will be significantly higher among the NF cohort than a matched control cohort. We have identified approximately 180 NF patients that will meet inclusion criteria. This clinical data is linked with administrative data at the Manitoba Centre for Health Policy. Cases will be matched 1:5 with controls from the general population based on age, sex and geographical region and aggregated diagnostic group, an indicator of co-morbidities. Outcomes will be both diagnoses and associated health care utilization for the 2 years’ duration prior compared to the 2 years’ duration post NF injury. We expect that NF patients will have a significantly higher incidence of mental health diagnosis and associated health care utilization compared to the match control cohort 2 years pre- and post-NF infection.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".