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Record W2027836289 · doi:10.1136/bmj.324.7340.793/e

Kathleen Mary McPhillips (née Casey)

2002· article· en· W2027836289 on OpenAlexaboutno aff
C. M. Hapnes

Bibliographic record

VenueBMJ · 2002
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychoanalysisArtPsychology

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.156
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.1560.057

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.

Opus teacher head0.034
GPT teacher head0.299
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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