Bibliographic record
Abstract
Editor—The BMJ 's decision to extend participatory democracy to the question of disease is important not so much for the results but because it happened at all.1 To a previous generation the idea of asking consumers to decide on these matters would have been incomprehensible. Doctors decided which conditions were legitimate and which should be consigned to the outer darkness. In the debate about the nature of neurasthenia at the end of the 19th century all protagonists were in the medical profession and their debates were published in journals. The views of a few well educated and well heeled patients may be inferred from diaries and fiction, but their voices were largely unheard and unheeded. Now of course medical authority is in retreat everywhere and the final arbiter of “non-disease” is fast becoming the patient. All this is well and good, so why the outrage of so many respondents?1 I suspect it comes from a failure to recognise the different concepts of illness and disease. Taking chronic fatigue syndrome as an example from the debate,1 few could now question that it is indeed an illness. It has a nosological status and is clearly associated with suffering, ill health, and disability. The patient's voice must be and is paramount. But is it a disease—that is, has a specific pathological process been identified to account for the above? Chronic fatigue syndrome is not yet a disease because no unambiguous evidence has yet been presented that has commanded widespread acceptance by the scientific community, which remains the arbiter. Of course, the syndrome may plausibly make the transition from illness to disease like many other illnesses have done. Or it may not. The traffic is not entirely one way in which illness entities inevitably receive the stamp of scientific approval, usually after a period of being falsely labelled as psychological. Previously apparently sound entities have lost their disease status under the cold light of scientific scrutiny. The concept of labelling also generated a lot of heat in this debate. People behave according to the labels that are ascribed to them, a process seen as largely negative. Some respondents rightly echo this, citing examples in which the act of labelling distress as something medical (pathological) carries with it a host of adverse consequences.1 w1-w8 But more commonly the act of giving a name to symptoms and disability brings relief.w9-15 The acknowledgement by the medical profession that a patient's condition has a name and is a legitimate illness is immensely reassuring and enabling. It also ends the battle of diagnosis—“If you have to prove you are ill you can't get well.”w16 Giving a condition a name is an intervention in itself with costs and benefits.w17 Crudely handled, medicalisation can perpetuate disability and exclusion. But used constructively and appropriately it is the first step towards recovery.
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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".