Trustworthy guidelines – excellent; customized care tools – even better
Bibliographic record
Abstract
BACKGROUND: The ability to do online searches for health information has led to concerns that patients find the results confusing and that they often lead to expectations for treatments that have little supportive evidence. At the same time, the science of summarizing research evidence has advanced to the point where it is increasingly possible to quantify treatment tradeoffs and to describe the balance between harms and benefits for individual patients. DISCUSSION: Trustworthy clinical practice guidelines provide evidence-based recommendations to health care practitioners based on assessments of study-level averages. In an effort to customize the use of evidence and ensure that choices are consistent with their personal preferences, tools for patients have been developed. Gradually, there is recognition that the audience for high quality evidence is much wider than merely health care professionals - and that there is a case to be made for creating tools that translate existing evidence into tools to help patients and clinicians work together to decide next steps. We observe two processes occurring: first, is the recognition that decision making in healthcare requires collaboration and deliberation, and second, to achieve this, we need tools designed to customize care at the level of individuals.
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 | 0.139 | 0.481 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".