MEDICAL-ECONOMIC APPROACH TO MASS SCREENING FOR COLORECTAL CANCER IN THE WILAYA OF BEJAÏA
Bibliographic record
Abstract
Abstract The economic evaluation finds its justification in the impossible market regulation of the health sector. Most sectors of the economy are in fact governed by market mechanisms which encourage consumers and producers to make the best use of the limited resources available to society, according to their own interests. The health sector escapes this mode of operation, in particular because of the difficulty for the consumer to access all the information allowing them to judge the characteristics (quality and price) of the health good or service which would be beneficial to them. We were called upon to carry out a mass screening strategy for colorectal cancer in the wilaya of Bejaia, this screening concerned more than 3000 citizens, the crucial and decisive step for maintaining this screening was to evaluate the cost-effectiveness of this operation which aims primarily to reduce mortality and the incidence of this cancer which remains a public health problem in Algeria. The main objective of this article is to have all the cost data linked to screening or management of CRC only in order to be able to estimate the different cost items linked to the disease and to ensure the medico-economic evaluation. organized screening carried out between 2017 and 2020 as part of the Algerian cancer plan in the wilaya of Bejaia. Screening for CRC with an immunological test cost more than 12 million dinars. Direct medical costs represent more than two thirds of the cost, or a rate of 69.6%. This study evaluated the cost-effectiveness ratio for a single screening strategy in the general population (50-74 years) based on the FIT. Direct and indirect costs were estimated from different sources. However, the problem deserves to be mentioned and taken up again in a few years. Such economic quantification is difficult, in particular because of the great complexity of estimating the cost of cancer in Algeria, including colorectal cancer, but also because of the absence of an epidemiological benchmark, notably data on mortality from cancer. colorectal cancer.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".