Do Patients Registered with CAM-Trained GPs Really Use Fewer Health Care Resources and Live Longer? A Response to Kooreman and Baars - European Journal of Health Economics (2012), 13:469-776
Bibliographic record
Abstract
We read with interest the article by Kooreman and Baars, which aimed to explore the cost-effectiveness of CAM (complementary and alternative medicine) compared with conventional medicine. More specifically, this paper compared health care costs and mortality rates across two patient populations; the primary distinguishing feature being whether or not patients' general practitioners (GP) had completed certified additional training in CAM. The paper addresses an important and thought-provoking issue, and adds to a relatively small (though not neglected) area of health economics research. The authors assert that patients registered to a CAM-GP have lower health care costs and mortality rates. Although specific policy implications are not discussed in the article, one would assume that the authors would infer that their results provide support for CAM on the grounds of cost-effectiveness. We believe these findings could be widely cited, as is commonplace for supportive CAM research. For this reason we feel that further discussion is necessary, particularly with regard to the methods of analysis and the reporting of empirical results.
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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 | 0.023 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.026 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".