Chloramphenicol or ampicillin plus gentamicin for the treatment of very severe pneumonia
Bibliographic record
Abstract
New products usually offer advantages over existing products, but in health care, most new drugs are 'me-too', comparable in effectiveness and side effects to existing drugs, but with a more ambiguous evidence base around adverse effects. Despite this, new treatments drive increased health care spending, suggesting a preference for 'newness' in this setting. We explore (1) whether preferences for treatments labeled 'new' exist and (2) persist once the ambiguity in the evidence base reflecting newness is described. We use a Canadian general population sample (n = 2837) characterized by their innovativeness in adopting new products in normal markets. We found that innovators/early adopters (n = 173) had significant preferences for 'newer' treatments (B = 0.162, p = 0.038) irrespective of comparable benefits and side effects and all respondents had significant preferences for less ambiguity in benefit/side effect estimates. Notably, when 'newness' was combined with ambiguity, no significant preferences for new treatments were observed regardless of respondent innovativeness. We conclude that preferences for new products exist for some people in health care markets but disappear when the implication of ambiguity in the evidence base for new treatments is communicated. Physicians should avoid describing treatments as 'new' or be mindful to qualify the implications of 'new' treatments in terms of evidence ambiguity. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".