Bibliographic record
Abstract
Advocate of evidence based health care who led moves to reduce medical errors and improve patient safetyThe death of John Eisenberg, who was director of the United States Agency for Healthcare Research and Quality (AHRQ), leaves a void in the international healthcare research and quality movement.A man of seemingly boundless energy, John died from a brain tumour, first diagnosed over a year ago.Until the last few weeks of his life, he kept up a full workload, while his gracious family, friends, colleagues, coworkers, and people whom he had mentored in the United States and around the world came to see him.During his tenure (1997)(1998)(1999)(2000)(2001)(2002) as AHRQ head in what sadly proved the twilight of his life, John's accomplishments were amazingly multifaceted (www.ahrq.gov/news/jme/index.html).He enthusiastically built a rock-solid evidence based practice centre (EPC) programme.The programme got rolling shortly after he assumed his position.At the time of his death, 12 centres in the United States and Canada had generated 56 evidence reports.As John envisaged these centres, they addressed areas in medicine marked by broad practice variation and uncertain value.He was adamant that the evidence reports should not go beyond the data and that users should take the findings to carve out their own health quality initiatives or guidelines.AHRQ's mission was to address high cost, big ticket items for Medicare.Concerned that limited healthcare dollars could be tossed to the wind, the EPC programme was one of a slew of vehicles that John used to help teach people how to disseminate best practices, identify problems in practice, and move ahead to a higher quality of care."The force of his talent, personality, intellect, and leadership elevated both the office and the field," said Alan M Garber, staff physician at the VA Palo Alto Health Care System and director of the Center for Health Policy at Stanford University."By making AHRQ the federal leader in quality improvement and patient safety, he made the lives of millions of Americans better in a direct and tangible way.He was a warm and
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.156 | 0.057 |
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".