Bibliographic record
Abstract
Douglas Wardrop (JRSM 2008;101:50–1) is right in commenting that the scholastic logic of Ockham's razor is of limited relevance to geriatric medicine. Geriatricians have long taught their students that if an old person admitted to hospital has less than 5.5 or 6.5 diagnoses he or she (respectively) may have been inadequately assessed. But I was surprised to see so little deference to probability in the Editorial. It is not the number of different ‘entities’ – a fine scholastic concept – but their probabilities that matters. One of my patients with ulcerative colitis developed a peripheral arthropathy that the literature claimed as a rare complication of chronic bowel disease. Calculation revealed that it was much more likely that she had rheumatoid arthritis as well as her bowel disease rather than the single diagnosis decreed by Ockhamism. Conversely, decades before Lewy Body Disease was defined it was obvious that Parkinsonism (clinically diagnosed) and dementia (also clinically diagnosed) occurred together far more often than was compatible with independent incidence. This prophesied the existence of at least one unrecognised unifying ‘entity’. But probabilities varying with sex, age and other demographic factors can be difficult to estimate from conventional medical literature. How much easier both clinical diagnosis and research would be if the NHS had an accessible system of epidemiologically structured medical records matching that of an American Health Maintenance Organization. We could then forget the scholastics.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".