Implementation and evaluation of a quality and safety tool for ambulatory strongyloidiasis patients at high risk of adverse outcome
Bibliographic record
Abstract
Strongyloidiasis is a common infection in Canadian migrants that can cause life-threatening hyperinfection in immunosuppressed hosts. We designed and implemented a safety tool to guide management of patients with Strongyloides in order to prevent adverse outcomes. Methods: Patients treated at our centre for strongyloidiasis from January 1, 2013 to December 31, 2015 were identified through our ivermectin access log. Patients were categorized into pre-implementation and post-implementation groups. A retrospective chart review for predefined variables was conducted. Of 37 patients with strongyloidiasis, 26 were in the pre-implementation group and 11 were in the post-implementation group. Documented seroreversion (positive to negative) occurred in 42.1% of patients pre-implementation and 62.5% of patients post-implementation ( p = 0.420). Documented stool clearance occurred in 80.0% of patients pre-implementation and 100.0% of patients post-implementation ( p = 1.000). More patients were screened for HTLV-1 coinfection post-implementation (80.0%) versus pre-implementation (30.8%) ( p = 0.011). Loss to follow-up after treatment occurred in 23.1% of patients pre-implementation and 20.0% of patients post-implementation ( p = 1.000). The safety tool may be useful in the treatment of patients with strongyloidiasis to improve documentation of patient outcomes and standardize care. Future research should include a powered prospective study.
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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".