Prognostic Nomograms To Predict Oncological Outcome of Thyroid Cancers
Bibliographic record
Abstract
CONTEXT: Thyroid cancers represent a conglomerate of diverse histological types with equally variable prognosis. There is no reliable prognostic model to predict the risks of relapse and death for different types of thyroid cancers. OBJECTIVE: The purpose of this study was to build prognostic nomograms to predict individualized risks of relapse and death of thyroid cancer within 10 years of diagnosis based on patients' prognostic factors. DESIGN: Competing risk subhazard models were used to develop prognostic nomograms based on the information on individual patients in a population-based thyroid cancer cohort followed up for a median period of 126 months. Analyses were conducted using R version 2.13.2. The R packages cmprsk10, Design, and QHScrnomo were used for modeling, developing, and validating the nomograms for prediction of patients' individualized risks of relapse and death of thyroid cancer. SETTING: This study was performed at CancerCare Manitoba, the sole comprehensive cancer center for a population of 1.2 million. PATIENTS: Participants were a population-based cohort of 2306 consecutive thyroid cancers observed in 2296 patients in the province of Manitoba, Canada, during 1970 to 2010. MAIN OUTCOME MEASURES: Outcomes were discrimination (concordance index) and calibration curves of nomograms. RESULTS: Our cohort of 570 men and 1726 women included 2155 (93.4%) differentiated thyroid cancers. On multivariable analysis, patient's age, sex, tumor histology, T, N, and M stages, and clinically or radiologically detectable posttreatment gross residual disease were independent determinants of risk of relapse and/or death. The individualized 10-year risks of relapse and death of thyroid cancer in the nomogram were predicted by the total of the weighted scores of these determinants. The concordance indices for prediction of thyroid cancer-related deaths and relapses were 0.92 and 0.76, respectively. The calibration curves were very close to the diagonals. CONCLUSIONS: We have successfully developed prognostic nomograms for thyroid cancer with excellent discrimination (concordance indices) and calibration.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".