Medication Adherence: Most Important but Mostly Despised
Bibliographic record
Abstract
In the UK, up to 50% of medicines are not taken as intended and 60% of NHS patients failed to receive the right treatment within 18 weeks. Lack of medication adherence leads to poorer health outcomes, higher healthcare expenditures, increased hospitalizations, and even higher mortality rates in patients with chronic diseases. Medication non-adherence alone accounts for at least 10% of hospitalizations in US, 250,000 hospitalizations in Australia and 1.1 million hospital days in France; induces $300 billion in annual medical costs in US, and $125 billion in EU; causes more than 1,25,000 premature deaths in the US and 2,00,000 deaths in EU. Also, two-thirds of medication-related hospital admissions in Australia are potentially preventable. A recent Canadian study found that 30% of patients stop taking their medication before it is instructed, and 25% patients do not fill their prescription or take less than prescribed. Among patients with at least one preventable encounter, medication non-adherence was associated with $679-$898 increased preventable spending. However, pharmaceutical companies around the globe lost $637 billion in potential sales annually due to non-adherence, with $250 billion lost for the same in the U.S. alone last year.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".