Bibliographic record
Abstract
Abstract Lung cancer is best conceptualized as a group of heterogeneous clinical entities with common molecular and cellular origins, but with different clinical behaviors, and hence, different prognoses. Lung cancer has a major impact on population health; taken together, lung cancers are the most common cause of cancer death in both males and females in North America. Overall, only 13% of patients survive more than 5 years after being diagnosed. Determining the prognosis for an individual patient with lung cancer is difficult, in part, because the clinical course of the disease in the individual case can evolve along a seemingly infinite combination of branching pathways. Over 90% of lung cancer patients present with signs or symptoms of disease, and since no symptoms or signs are pathognomonic for lung cancer, the disease has a capacity clinically to mimic other cancers and other nonmalignant diseases with a constellation of possible presenting symptoms. These varying presentations, and potential clinical evolutions, are in turn due to the multiple potential manifestations of the primary tumor, metastatic sites of involvement, and paraneoplastic syndromes. Despite the heterogeneity of the clinical manifestations of lung cancer, the prognosis for a population of patients with lung cancer is remarkably predictable. The overall mortality rates for lung cancer in North America over the last 15 years have remained unchanged. A population‐based study of over 12,000 patients with unresected non‐small‐cell lung cancer registered in seven Ontario Regional Cancer Centers (1982–1991) demonstrated no significant differences in patient survival either between Centers or over time. The predictability of population survival outcomes is of limited usefulness, however, due to the marked heterogeneity of patients making up the overall population. Prognostic factors are thus used to divide the overall population of lung cancer patients into subgroups in order to realize the benefits of prognostic stratification. A number of studies have addressed the identification and application of such factors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.018 | 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 teacher head, 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".