Bibliographic record
Abstract
On March 23, 2010, during a symposium on prescription drug misuse, a lovely and dignified, but sad couple told the story of losing their beloved daughter to an overdose of prescription drugs. She was the light of their lives and they were very proud when she left home to go to University. Unfortunately, she was involved in a car accident, was prescribed strong pain killers for her injuries and continued to take them after she had physically recovered. Within six months and without anyone noticing, she was obtaining prescriptions from multiple doctors and filling them in multiple pharmacies. Her addiction eventually led directly to a death from overdose of the pain killers. A Medical Officer of Health who listened to the story approached the parents once the meeting had finished and thanked them for sharing their memories of their daughter. He asked: “It has been years now since her death and you obviously still grieve when you tell her story. Why do you continue to re-live this? ” The father answered for both of them with quiet dignity, “We just want to make sure that someone does something to ensure that no one else’s daughter dies this way. We want to make sure her death means something. We want someone to take responsibility for preventing this.” The Medical Officer of Health reflected afterwards on the wisdom of the parent’s statement. It was a preventable death; the Government of Alberta had paid for the physician visits, the prescriptions, the rehabilitation and the cost of an autopsy. He also reflected that while many groups have a role to play in ensuring this doesn’t happen again,
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.092 | 0.023 |
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".