Bibliographic record
Abstract
To the Editor: ‘One wonders why it is still widely used, given the lack of benefit and the potential for harm’ asks G. Jackson in his brilliant editorial about the ‘alternative’ form of chelation therapy for arteriosclerotic diseases (1). One could ask the very same question about many other alternative or complementary therapies. My systematic reviews of chelation therapy concluded years ago that this approach should be considered obsolete (2, 3). Despite the fact that these were published in major journals, this conclusion was ignored by those US enthusiasts who initiated the Trial to Assess Chelation Therapy (TACT) trial. As a consequence, US$3 million have been wasted. Before we feel too smug about political pressure reigning over evidence in the USA, we should ask ourselves what is happening in Britain. Here, we have a lobby group for alternative medicine, ‘The Prince’s Foundation for Integrated Health’, that managed to obtain public funds for publishing a promotional and misleading brochure about several disproven treatments (4). And even the RCP has a committee, now called ‘Integrated Health Committee’ that, since its creation in 2001, seems to have all but abandoned any critical evaluation of this area.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.065 | 0.075 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".