Case 12 : Policy Meets Practice – People Who Inject Drugs (PWID)
Bibliographic record
Abstract
Dr. Silverman is the Chief of Infectious Diseases at London Health Sciences Centre (LHSC) and St. Joseph’s Health Care in London, Ontario. He is concerned about the increasing prevalence of people who inject drugs (PWID) in London, and the risk to PWID of bacterial infections due to contamination (e.g., improperly or unsterilized injection equipment, skin not being sterilized before injection). Of primary concern is the risk of infective endocarditis (IE), an infection in a patient’s heart. Treatment for IE entails antibiotics administered through the intravenous (IV) route. IE is generally treated through home care; in London, the South West Community Care Access Centre (CCAC) is responsible for delivering home care. To treat IE at home, a patient would need a peripherally inserted central catheter (a PICC-line) and assistance from a CCAC nurse to administer the antibiotics. This option, however, is not viable for some patients, including those who fall under the category of PWID or who may not have a fixed address. In the case of PWID, the PICC-line, in effect, becomes a “highway” for injecting other drugs; in instances where a patient may not have secure housing or be homeless, the CCAC nurse may not be able to track down the individual. When a patient in one of these situations is being treated for IE, it puts the care team in a difficult position. The alternatives to home care are hospital admittance or no treatment at all, neither of which are ideal solutions. Dr. Silverman is currently in this position, as he must decide on a treatment plan for Mr. W., a patient who has IE, has struggled with drug addiction (the likely cause of his IE), and who does not have stable housing. In making his decision, Dr. Silverman has included on Mr. W.’s care team two other physicians from LHSC, a representative from the CCAC, and the managing director of London CAReS, a community-based housing-first organization. The care team must determine the best treatment plan for Mr. W.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".