Bibliographic record
Abstract
OBJECTIVES: Pancreatectomy is regarded as the only curative treatment for cancer of the pancreas. A population-based study was conducted to examine its efficacy within the general community. METHODS: Overall and cancer-specific survivals were compared between individuals treated with pancreatectomy and those managed nonsurgically. Kaplan-Meier analysis was used, based on both time from diagnosis and attained age (age at diagnosis plus time from diagnosis). RESULTS: A total of 7830 Surveillance Epidemiology and End Results cases of localized cancer of the pancreatic head were retrieved, diagnosed from 2000 to 2008. Median follow-up was 12 months; the pancreatectomy cohort was 5 years younger and had 7-fold less stage III disease. Overall and cancer-specific survivals were 17% and 21% at 5 years from time of diagnosis in the pancreatectomy cohort versus 2% and 4% in the nonsurgical cohort, respectively (P<0.001). However, the overall and cancer-specific survival curves were nearly superimposed on each other when based on attained age. Moreover, the proportion of deaths attributable to pancreatic cancer exceeded 85% in both cohorts. CONCLUSIONS: A lead-time bias is hypothesized to explain the survival discrepancies seen between time from diagnosis and attained age analyses; the pancreatectomy cohort was diagnosed earlier, with less disease. If most of these individuals had occult metastases at diagnosis, which manifested later and caused death at similar ages as the nonsurgical cohort, their survival from time of diagnosis would appear speciously improved. A randomized controlled trial would be necessary to confirm whether or not the survival advantage ascribed to pancreatectomy should be attributed to lead-time bias.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".