Annals of Surgical Oncology 15(7):1813–1814 DOI: 10.1245/s10434-008-9918-x Beyond the Ivory Tower: Outcomes Following Surgery for Ampullary Cancer
Bibliographic record
Abstract
A frequently uttered wisecrack amongst academic surgeons refers to those manuscripts in which the authors outnumber the study subjects. While ampullary carcinoma is rare, manuscripts on the subject do not usually engender this kind of criticism. As Dr. O’Connell and colleagues state in their manuscript, published in the current issue of Annals of Surgical Oncology: while uncommon, ampullary carcinoma represents the second most common cause of periampullary malignancy. 1 They utilized the Surveillance Epidemiology and End Results (SEER) tumor registry to analyze the outcomes of more than 3000 patients who underwent surgical resection for ampullary carcinoma. As the authors state, this represents the largest such analysis of outcomes for patients with this uncommon malignancy. They conclude that, compared to most series published in the last 15 years, SEER data reveals a lower resection rate, a higher overall mortality rate, and lower observed 5-year survival. These findings are not particularly enlightening in and of themselves, but rather serve as a gentle reminder of the fragile truths we accept daily in the practice of surgical oncology: that data derived from small, retrospective single-institution studies are replete with numerous biases. In the current study, the authors reference reports from revered institutions such as Johns Hopkins, Memorial Sloan–Kettering, and Toronto General. 2–4 It should not be surprising, but rather expected, that patient outcomes from these institutions might look different from those of a population treated in smaller centers, as is represented by
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.047 |
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".