Bibliographic record
Abstract
The Evidence-Based Medicine Manifesto aptly identifies massive ‘too much’ problems: too much hidden information, too much bias, too much research waste and too much overtreatment.1 2 One potential solution is to focus efforts on a small number of needed treatments while allowing all the excess to fall by the wayside. Here I explore the potential role of the essential medicines list (EML) in meeting some of the big challenges posed in the Manifesto. EMLs usually include around 300 medicines that meet the priority needs of a population.3 4 The WHO created its Model EML in 1977, and since then more than 100 countries have adapted the WHO’s Model EML to their own circumstances.3 4 Today more than five billion people live in a country with an EML. Ultimately we need to know which treatments should be used and EMLs are ‘positive lists’ that can help direct people to effective treatments (as opposed to ‘negative lists’ of medicines to avoid). Given all the clinical research that has been done over the decades, we ought to be able to write down a list of medicines that were proven to have important benefits without excessive harms in studies that were properly done and reported. The fact that it is …
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.036 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.021 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".