Concordância entre os estadiamentos clínico e patológico em pacientes com câncer de pulmão não-pequenas células, estádios I e II, submetidos a tratamento cirúrgico
Bibliographic record
Abstract
OBJECTIVE: To compare clinical and pathological staging in patients with non-small cell lung cancer submitted to surgical treatment, as well as to identify the causes of discordance. METHODS: Data related to patients treated at the Department of Thoracic Surgery of the Pontifical Catholic University of Rio Grande do Sul São Lucas Hospital were analyzed retrospectively. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for clinical stages IA, IB, and IIB. The kappa index was used to determine the concordance between clinical and pathological staging. RESULTS: Of the 92 patients studied, 33.7% were classified as clinical stage IA, 50% as IB, and 16.3% as IIB. The concordance between clinical and pathological staging was 67.5% for stage IA, 54.3% for IB, and 66.6% for IIB. The accuracy of the clinical staging was greater for stage IA, and a kappa of 0.74, in this case, confirmed a substantial association with pathological staging. The difficulty in evaluating nodal metastatic disease is responsible for the low concordance in patients with clinical stage IB. CONCLUSIONS: The concordance between clinical and pathological staging is low, and patients are frequently understaged (in the present study, only one case was overstaged). Strategies are necessary to improve clinical staging and, consequently, the treatment and prognosis of patients with non-small cell lung cancer.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".