Histomorphological spectrum of oral squamous cell carcinoma in a tertiary care centre: A four-year study
Bibliographic record
Abstract
Indian cancer statistics, a model to be followed In India, around 555 000 people died of cancer in 2010, according to estimates published in The Lancet today ( 1 ) (March 28, 2012).The study, led by Dr Prabhat Jha, the Director of the Centre for Global Health Research at St. Michael's Hospital, Toronto, in a collaboration with Indian national institutions and the International Agency for Research on Cancer (IARC), used a unique method of projecting cancer deaths for the whole of India based on the patterns of cancer mortality in 2000-2003 in a sample of households.Cancer mortality is a key measure of the cancer burden in a given country and provides an important basis for implementing public health preventive measures.India is the first of the emerging economies to join IARC in 2006, and is an active Participating State of the global cancer research agency.This landmark study, as well as providing a unique snapshot of the current Indian situation with respect to cancer mortality, paves the way for other emerging economies to implement similar systems in settings where the civil death registration systems (CRS) are either non-existent or too weak to provide reliable information on the numbers and causes of deaths.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".