Innovation in medical care: examples from surgery
Bibliographic record
Abstract
C, a newborn infant, develops persistent vomiting on the second day of life. X-rays show midgut volvulus, a condition in which the intestines have twisted around their blood supply. Surgical exploration reveals necrosis of all but 15 cm of his small bowel. The necrotic bowel is removed and total parenteral nutrition (TPN) is initiated. At one year of age, he is taking half of his nutritional needs through his intestinal tract; the other half is given intravenously. Blood chemistry tests show that he is starting to develop significant liver damage from the TPN. C's remaining small bowel has become dilated and dysfunctional. You have recently read about a new operation called the serial tapering enteroplasty (STEP), an innovative technique, which may be able to lengthen the remaining intestine and permit it to function more effectively. A surgical stapler in common use is deployed to segment the dilated bowel into a tapered, lengthened tube more closely resembling the shape of the small intestine (Kim et al., 2003). This operation, first developed in dogs, has been undertaken in a small number of infants with short bowel syndrome. It is considered a non-validated innovation by most pediatric surgeons and is not yet accepted as part of standard surgical practice. You would like to offer the procedure to your patient, but you do not think that there is time to go through the full Research Ethics Board approval process at your hospital. Your intention is to try to help, and perhaps other patients like him.[…]
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".