[Standard treatment cost of female breast cancer at different TNM stages].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the standard treatment cost of female breast cancer at different tumor node metastasis (TNM) stages. METHODS: Extracting previous data, calculating by clinical pathway, face-to-face interviewing, and telephone interviewing were adopted to estimate the treatment cost of female breast cancer. The cost was consisted of direct medical expenditure, direct non-medical expenditure, and indirect expenditure. RESULTS: The direct medical expenditure was extracted from medical record and expense statement of 316 breast cancer cases in Sichuan Cancer Hospital. The direct non-medical expenditure was investigated from 211 patients and their relatives. The indirect expenditure was surveyed from 181 cases who received surgery more than one year ago. The average treatment cost of female breast cancer was ¥160 457 ($23 702), which was adjusted by the proportions of ER, PR, and menses status, and the willingness of patients. The treatment cost (including the outpatient cost for 5 years after surgery, radiotherapy, and chemotherapy) of TNM 0 stage, TNM I stage, TNM II stage, TNM III stage, and TNM IV stage were ¥37 941, ¥122 622, ¥159 594, ¥215 014, and ¥214 229, respectively. The patients with early stage breast cancer payed considerably lower treatment cost than those at advanced stage. CONCLUSION: Early detection and treatment of breast cancer may have a real economic significance for reducing the burden of disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".