The paths from research to improved health outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evidence-based practice aims to provide clinicians and patients with choices about the most effective care based on the best available research evidence. To patients, this is a natural expectation. To clinicians, this is a near impossible dream. The US report Bridging the quality chasm has documented and drawn attention to the gap between what we know and what we do.1 The report identified 3 types of quality problems—overuse, underuse, and misuse. It suggested “The burden of harm conveyed by the collective impact of all of our healthcare quality problems is staggering.” Although attention has focused on misuse (or error), a larger portion of the preventable burden is likely to be the evidence-practice gaps of underuse and overuse. Research that should change practice is often ignored for years—for example, crystalloid (rather than colloid) for shock,2 supine position after lumbar puncture,3 bed rest for any medical condition,3 and appropriate use of anticoagulants and aspirin in patients with atrial fibrillation.4 Antman et al documented the substantial delays between cardiovascular trial results and textbook recommendations.5 However, even when best practices are well known, they are often poorly implemented: national surveys show that most hypertensive patients are undetected, untreated, or inadequately controlled,6 which has led to the current interest in knowledge translation.7 What role does evidence-based practice 8 have in bridging the research-practice gap? Surveys of clinicians suggest that a major barrier to using current research evidence is the time, effort, and skills needed to access the right information among the massive volumes of research.9 Even for a (mythical) up to date clinician, the problem of maintaining currency is immense. Each year Medline indexes >560 000 new articles, and Cochrane Central adds about 20 000 new randomised trials. This is about 1500 new articles and 55 …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it