Additional file 1 of Influence of physician networks on the implementation of pharmaceutical alternatives to a toxic drug supply in British Columbia
Bibliographic record
Abstract
Additional file 1: Supplementary Appendix. Table A1. Databases used to construct the cohort. Table A2. Drug identification numbers for identification of opioid agonist treatment from PharmaNet. Table A3. Diagnostic codes used for opioid and non-opioid substance use disorders to identify substance use disorder clients. Table A4. Drug identification numbers for identification of possible prescribed safer supply. Table A5. Algorithms to identify prescribed safer supply prescriptions. Table A6. Logistic regression results for probability of PSS uptake between May 1st 2020 – August 31st 2021, with the PSS peer exposure redefined as proportion of patients shared with PSS prescribing peers’. Table A7. Logistic regression results for probability of PSS uptake with a longer lagged period: July 1st 2020 – August 31st 2021. Table A8. Logistic regression results for probability of PSS uptake between May 1st 2020 – August 31st 2021, with an additional exposure controlling for PSS prescribers with 2 degrees of separation. Table A9. Logistic regression results for probability of uptake of different PSS medication times between May 1st 2020 – August 31st 2021. Table A10. Logistic regression results for probability of PSS uptake for prescribers with more than one client with substance use disorder in the month prior. Table A11. Logistic regression results for probability of PSS uptake for prescribers with at least five clients with a substance use disorder in the month prior. Table A12. Logistic regression results for probability of PSS uptake under the more specific case-finding algorithm. Table A13. Logistic regression results for probability of PSS uptake when ending the calendar month on the 15th of each month.
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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 | 0.001 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.668 | 0.057 |
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".