Bibliographic record
Abstract
It is an art of no little importance to administer medicines properly; but it is an art of much greater and more difficult acquisition to know when to suspend or altogether omit them" (Pinel 1809).A s Pinel's quote illustrates, the understanding that less is sometimes more in healthcare is not new, but there is growing energy behind efforts to advance appropriateness of care across the health sector.Ensuring the right care for each person -neither more nor less -is the goal.In some ways, the challenge is growing over time because complex care patterns are an increasing reality in developed countries.The 2014 Commonwealth Fund survey, for instance, found that between 8% and 25% of seniors across 11 countries saw four or more doctors in the last year.And between 29% and 53% took four or more prescription medications.In both cases, Switzerland had the lowest rates and the US the highest.Likewise, the Canadian Institute for Health Information (2014) reports that nearly two-thirds of seniors took five or more prescription medications in 2012.More than one-quarter (27.2%) had claims for 10 or more medications.With complex care patterns comes a need for strong coordination of care.Across developed countries, patients who say that they saw four or more physicians are at least twice as likely as those with one or two physicians to report having experienced a medical mistake, medication error, or laboratory test problem in the past two years (Commonwealth Fund 2010).And in Canada in 2012, 24% of those aged 65 and older -or more than a million people -had been prescribed a medication that was potentially inappropriate for seniors (CIHI 2014).The statistics are clear, but there is nothing like personal experience to bring home the human costs involved.Two years ago, a relative of mine had been prescribed a complicated cocktail of medications.It was a true medication cascade, built up over time as clinicians added new drugs to address side effects generated by medications prescribed by others.Several falls, long hospitalizations, and broken bones later, she moved into long-term care.Her de-prescribing journey, initiated by a geriatrician during her last hospital stay, continues.And it has been a journey, complete with starts and stops, progress and setbacks.A recent Canadian Institutes of Health Research meeting for stakeholders keen to improve safe and appropriate medical therapy for older men and women drew attention to the range of approaches that can be taken to advance this goal.Different strategies focus De-prescribing: When Less Is More in Healthcare
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.034 | 0.057 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".