Top POEMs of 2015 Consistent with the Principles of the Choosing Wisely Campaign.
Bibliographic record
Abstract
The authors applied a novel method to identify recent clinical studies that showed results consistent with the principles of the Choosing Wisely campaign. The method, based on crowdsourcing studies known as POEMs (patientoriented evidence that matters), involved analyzing POEM ratings submitted by physician members of the Canadian Medical Association in the context of their continuing medical education. In the 251 unique POEMs delivered to these physicians in 2015, an average of 1,284 physician ratings were received per POEM. The authors then identified the POEMs that ranked highest on a single item in the rating questionnaire-namely, whether the POEM helps reduce overdiagnosis or overtreatment, which is the focus of the Choosing Wisely campaign. The result is a set of POEMs of original research that describe interventions that are not superior to other options, are sometimes more expensive, or place patients at increased risk of harm. Knowing the bottom line of these studies could help physicians and patients engage in better conversations when making decisions about clinical care.
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 | 0.036 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".