Rebuttal to Dr Streiner: Can the “Evil” in the “Lesser of 2 Evils” Be Justified in Placebo-Controlled Trials?
Bibliographic record
Abstract
D r Streiner admits to possible harm to participants in placebo-controlled trials with his statement that they are a "necessary evil," even when effective drugs exist.His justification rests on his supposition that they produce "unambiguous" results, "fewer people are exposed to ADRs [adverse drug reactions]," and "the number of people receiving ineffective treatment is likely lower."He points to many flaws that occur in clinical trials that may adversely impact on the trial results: selection of inappropriate patients as subjects, problems with randomization, inadequate blinding, failure to give a therapeutic dosage of comparator drugs, incomplete follow-up, high drop-out rates, and incomplete or inaccurate reporting of trial results.While published trial results may inaccurately portray difference or no difference for one or several of these reasons, introducing a placebo control will not make up for these inadequacies.In fact, in some cases, it will exacerbate them.
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 | 0.012 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.098 | 0.081 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".