FreeMedForms: Managing drug-drug interactions. An open source model.
Bibliographic record
Abstract
The FreeMedForms project includes the FreeDiams application. FreeDiams is a free and open source pharmaceutical drug prescribing assistant that manages patient drug allergies, drug intolerances and drug-drug interactions in both the drug selection and prescribing steps. FreeDiams manages multiple drug databases (FDA, French, Canadian, South African). FreeDiams is world-unique free and open source software that manages drug-drug interactions informed by referenced scientific sources. Technically, FreeDiams is FreeMedForms' EMR drug assistant plugin, built as a standalone application. Its computations are fully integrated with FreeMedForms but can, at the same time, be linked to any application thanks to its command line parameters. GNUmed has adopted FreeDiams as a "drug expert system". Recently, the FreeMed and SynapseEHR teams began work to port FreeMedForms' drug interaction engine/data to an open source webportal for drug-drug interactions-checking.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.073 |
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".