Bibliographic record
Abstract
In a scientifically ideal randomized controlled trial (RCT) on the efficacy of screening for lung cancer, screening-detected cases would be allocated to immediate intervention or to no action until symptoms lead to diagnostics. The study would provide for learning about the extent to which earlier interventions (defined by disease stage and stage-conditional tumor size) enhance curability, and also about the distributions of disease stage and stage-conditional tumor size at the time of diagnosis under the particular regimen of screening and its associated diagnostics. Because ethics call for randomization to screening or no screening, this contrast no longer provides for studying the curability function of shared concern for all regimens of screening; it addresses only the overall curability advantage specific to the regimen deployed. The same information that is provided by the scientifically ideal RCT is obtainable from a noncomparative study in which each member of the study cohort is subject to both screening and early intervention, so long as the problem of "overdiagnosis" is avoided by documenting growth before biopsy for cytologic or histologic criteria of malignancy and so long as the outcome of intervention is documented by follow-up. The ethically feasible RCT, in addition to compromising the objects of study, involves validity problems of its own and is less efficient by an order of magnitude.
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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".