Bibliographic record
Abstract
Which gastrointestinal symptoms are useful in distinguishing organic from functional disease? Patients and clinicians are becoming increasingly intolerant of diagnostic uncertainty. This is reflected in the rising demand for endoscopic procedures1 implicitly suggesting that gastrointestinal symptoms are an unreliable indicator of serious pathology. Unfortunately, the growing array of tests that are being demanded for patients are placing further pressures on already stretched health care budgets. The natural reaction to this is to evaluate whether we can improve on the value of the history to diagnose gastrointestinal disease. This process was started over 30 years ago when researchers such as Card and colleagues2,3 and de Dombal and colleagues4,5 evaluated the use of computers to aid the clinician in making diagnoses in patients with upper gastrointestinal symptoms. The enthusiasm for this approach faded when data suggested that computer improved diagnostic accuracy of computers was not sufficient to prevent investigations.6 The paucity of subsequent research in this field is disappointing as it would be useful to know what information computers were using that enhanced diagnostic acumen. The article by Hammer and colleagues7 in this issue of Gut [see page 666] is therefore refreshing as it prospectively evaluates a wide range of gastrointestinal symptoms to establish which are useful in distinguishing organic from functional disease. The strength of this study is that it evaluates a large relatively unselected group of patients, reducing spectrum and selection bias.8 It also divided patients into those with and without organic disease rather than subdividing the data into different diagnoses. This improves the power of the study and gives the clinician overall information on whether the …
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 | 0.034 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.011 | 0.033 |
| Insufficient payload (model declined to judge) | 0.014 | 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".