The changing face of thyroid cancer in a population-based cohort, 2011-2015
Bibliographic record
Abstract
In North America, the incidence of thyroid cancer is increasing by over 6% every year. A population based cohort of 2306 consecutive thyroid cancers (170-2010) has already been established and followed in the province of Manitoba, Canada for a median period of 10.5 years. There has been a change in the treatment recommendations for thyroid cancer over the past 4 decades with more use of total thyroidectomy and radioactive iodine. The trends and factors influencing thyroid cancer incidence, its clinical presentation, and treatment outcome of 2306 patients seen during 1970-2010 will be compared with that of 575 patients from 2011-2015. The data from 2306 patients seen during 1970-2010 is already available and this seen during 2011-2015 project will involve review of electronic and paper charts of 575 patients. Age standardized incidence rate (ASIR) will be used to evaluate any change in the incidence of thyroid cancer. Disease specific survival and disease free survival will be estimated by the Kaplan-Meier method and intergroup comparisons will be made by log rank test. The independent influence of various prognostic factors will be evaluated by Cox Proportional Hazard models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".