Evidence or eminence: optimising patient outcomes by choosing the right doctor?
Bibliographic record
Abstract
Ulcerative colitis (UC) is a chronic inflammatory disease of the large bowel, characterised by a relapsing-remitting course, frequently requiring hospitalisation.1 Despite significant advances in therapy over the past decades, a significant proportion of the patients still come ultimately to colectomy. Relatively few data (around 25%) are available on the hospitalisation rates in UC; however, it appears that despite advances in treatment algorithms, hospitalisation and colectomy rates have not decreased in the last decade in UC. In general, hospitalisation rates are two–threefold higher compared with the general population.2 Moreover, stable hospitalisation rates have been reported from the US by using 1990 to 2003 National Hospital Discharge Survey data (8.2–12.4 per 100 000 people)1 and in a population-based cohort from Canada between 1994 and 2001 (12.6–13.3 per 100 000 people), with major surgery being an important reason for hospitalisation in the latter study (55%).3 By contrast, increasing hospitalisation rates have been reported in another study done in the US by analysing the nationwide inpatient sample data between 1998 and 2004,4 with an annual 3% increase in hospitalisation rates, although surgical rates remained stable. The reasons for hospitalisation in UC may be several, including infectious diseases, but these patients typically have acute severe colitis (ASC). ASC is a significant clinical condition affecting about 20% of UC patients during the disease course. Overall, the management of these patients is challenging. Murthy et al report an important study of the impact of gastroenterology specialist care compared with care by other providers …
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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 | 0.034 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".