14: Current Practice of Brachytherapy and External Beam Radiotherapy for Cervical Cancer in Ontario, Canada
Bibliographic record
Abstract
LYs and $271.3 billion (B) due to cervical cancer alone over the next 20 years.Based on a realistic linear investment model, RT yields an additional 9.8M LYs (2.9M in LIC, 4.7M in LMIC, and 2.2M in UMIC) over 20 years, a $53.2Bnet increase in economic productivity ($2.6B in LIC, $16.4B in LMIC, and $34.2B in UMIC), and a broader societal net gain of $137.5B($10.3B in LIC, $44.8B in LMIC, and $82.4B in UMIC).The additional investment necessary for HDR brachytherapy, an essential component of curative treatment, was only 5.5% greater than EBRT alone.Conclusions: The failure to ensure global availability of EBRT and BT to treat cervical cancer would result in enormous human and economic consequences over the next two decades.This loss would occur before the benefits of primary cancer prevention strategies, such as HPV vaccination, are realized.The present study demonstrates that a realistic investment strategy over the next 20 years may yield a net economic benefit of up to $150B USD, and potentially further benefits beyond that point in time.These findings support the value of scaling-up of EBRT and BT to treat cervical cancer and help to justify their inclusion in national cancer control planning.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".