Bibliographic record
Abstract
Resume Le cancer du poumon est une maladie qui frappe annuellement plus de 20 000 Canadiens. Il demeure la principale cause de deces par cancer au Canada. Le cancer du poumon non a petites cellules (CPNAPC), pour sa part, represente plus de 80 % des nouveaux cas de cancer du poumon nouvellement diagnostiques. Il s’agit d’une maladie au pronostic sombre puisque plus de 50 % des patients sont diagnostiques a un stade avance de la maladie (stade IIIb ou IV). En stade precoce (stade I ou II), la resection chirurgicale de la tumeur offre la meilleure chance de guerison complete de la maladie. D’un autre cote, la chimiotherapie a base d’un derive des platines s’avere un outil therapeutique important pour les patients presentant un CPNAPC en stade plus avance (stade III ou IV). Abstract Lung cancer is a disease that affects more than 20,000 Canadians each year. It is still the primary cause of death by cancer in Canada. First off, non small-cell lung cancer (NSCLC) is accountable for more than 80% of the newly-diagnosed cases. It is a disease with a very dark prognosis because more than 50% of patients are being diagnosed at a later stage (Stage IIIb or IV). In earlier stages (I or II), the surgical resection of the tumour offers the best chance of total recovery. From another perspective, chemotherapy based on a movement of plates proves to be a very important therapeutic tool for patients stricken by an advancedstage of NSCLC (Stage IIIb or IV). In the course of this article, we will bring you up to date as to what we know about non small-cell lung cancer. First, once again, we will take a good look at the histological classification of this type of cancer, the related-clinical symptoms, the etiology and the risk factors, the diagnosis, the stage determination of the disease and the prognosis. Then, we will elaborate on the various treatment methods currently used throughout the different stages of the disease.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".