Impact of GI/radiology conference on patient management
Bibliographic record
Abstract
Background: Multidisciplinary clinical GI/radiology meetings are held in most large general hospitals. The impact of these meetings on subsequent patient management is poorly characterised. Aims: To evaluate the impact of discussion of imaging at GI/radiology meetings on patient management Methods: Data were obtained from 95 consecutive patients discussed. The proposed management before discussion and change following discussion was recorded. Results: In 81/95 patients (86%), conferring facilitated diagnosis and subsequent management plan. The initial management plan was altered in 64/95 patients (67%). Surgery was avoided in 22 (23%) and postponed in 2 (2%). Radiological examination was advised in 19(20%), and avoided in 6 (6%). Endoscopic examination was recommended in 11 (11%), ERCP was avoided in 5 (5%). Surgery was recommended in 8 patients (8%). Biopsy was deferred in 5 (5%) and recommended in 1 patient. Pharmacological therapy was suggested for 4 patients (4%). An observant plan of management was undertaken in 3 (3%). Conclusions: The multidisciplinary GI/ radiology conference contributes significantly to patient care. Following discussion, the original management plan changed in 67%. Surgery was avoided in 23%. Further study is needed to determine the outcome of those patients whose management plan was altered following discussion at the meeting.
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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 | 0.009 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".