The concept of the ‘percent wasted patients’ in preventive health strategies
Bibliographic record
Abstract
PURPOSE: To better communicate the impact of poor persistence when preventive therapies are initiated, we propose a new measure that is intuitively understandable for clinicians. This measure is the percent wasted patients (PWP). METHODS: The PWP is the percentage of patients, out of the new users of a given preventive treatment, who have discontinued therapy before the time point at which clinical benefits become apparent on the cumulative incidence curves in randomized controlled trials (RCTs) comparing active treatment to placebo. To calculate the PWP, the RCTs that demonstrated the efficacy of the therapy under study must be identified from a MEDLINE search. The point at which the cumulative incidence curves for the main outcome in the experimental and placebo group start diverging is identified and is called the point of visual divergence (PVD). Then, using pharmaceutical claims databases, the percent persistence at the PVD in new users of the therapy is determined. The PWP is then calculated as 100% persistence at PVD. RESULTS: For primary prevention with statins, in the province of Quebec, the PWP is 44%. Of 100 patients starting statins for the primary prevention of coronary events, 44 will represent a waste in health resources because they will have discontinued therapy before any clinical benefit can be expected. CONCLUSIONS: The PWP is a simple measure that can be used by clinicians to select the therapies that need the most reinforcement concerning the importance of persistence.
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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 | 0.044 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".