Prognostic and clinicopathological significance of fibrinogen-to-albumin ratio (FAR) in patients with breast cancer: a meta-analysis
Bibliographic record
Abstract
eview question / Objective Fibrinogen-toalbumin ratio (FAR) has been extensively analyzed for its significance in predicting breast cancer (BC) patient prognosis, but existing findings remain conflicting.Therefore, the present meta-analysis was conducted for identifying FAR's significance for forecasting BC prognosis. Condition being studiedWe thoroughly searched databases PubMed, Embase, Web of Science, Cochrane Library, and CNKI till May 25, 2024.FAR's value for forecasting overall survival (OS) and disease-free survival (DFS) of BC was examined through computing combined hazard ratios (HRs) as well as 95% confidence intervals (CIs). METHODSParticipant or population Breast cancer patients diagnosed pathologically.Intervention Studies reported relation of FAR with BC patient prognosis and HRs as well as 95% CIs could be accessed or computed.Comparator BC patients with normal level of FAR. Study designs to be included Cohortstudies, including prospective and retrospectivecohorts. Eligibility criteriaThe studies below were included: (1) the BC cases were enrolled as study objects; (2) studies reported relation of FAR with BC patient prognosis; (3) hazard ratios (HRs) as well as 95% confidence intervals (CIs) could be accessed or computed; and (4) a cut-off value of FAR was identify.Studies below were eliminated: (1) meeting abstracts, reviews, letters, comments, case reports; (2) animal studies; and (3) studies recruited overlapped patients.There was no limitation on publication language.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".