What does it take to put an ugly fact through the heart of a beautiful hypothesis?
Bibliographic record
Abstract
Thomas Huxley characterised “the great tragedy of Science” as “the slaying of a beautiful hypothesis by an ugly fact.” Unfortunately, when medical hypotheses are disproven, they act more like zombies than corpses, revived by the sorcerers of Mammon, aided and abetted by the inertia of medical practice. Recent examples in diverse areas of clinical practice will likely suffer the problem of perseverance of beautiful but flawed hypotheses, so we are raising the alarm now. Three large trials in 2008 showed that intensive control of type 2 diabetes mellitus lacks benefits for patients and increases adverse effects1-3 (including one in this issue of Evidence-Based Medicine 4), and 2 trials showed that the self-monitoring of blood sugar in type 2 diabetes is not cost-effective5 and is associated with depression.6 Two trials have documented the lack of benefits of antiviral agents for Bell palsy (while confirming the benefits of corticosteroids).7 8 Many trials and meta-analyses have confirmed and reconfirmed the absence of benefits and presence of harmful effects of antioxidants for the prevention of cancer9 10 and cardiovascular disease.11 For each of these “slayings,” vested interests will undoubtedly try to make us forget that the justification for their promotions has been gored. For type 2 diabetes, …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.111 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.016 | 0.042 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".