Publication bias and quality of reporting of Pharmacodynamic Studies utilizing invasive research procedures within early phase cancer trials
Bibliographic record
Abstract
Invasive research procedures, such as biopsy for pharmacodynamic study, often have no value for patient-volunteers in terms of diagnosis or clinical management.Accordingly, their burdens are generally justifiable only by appeal to knowledge value (that is, the benefit to future patients) rather than direct benefit.This thesis is an exploration of knowledge value production as a justification for invasive research biopsy in cancer research.The premise of this thesis is that human investigations can only enable knowledge value insofar as they are reported in ways that enable a broader scientific community to use their findings for planning new investigations.We studied the interruption of knowledge value accrual through two empirically evaluable proxies of knowledge value: publication and reporting quality.risk for percutaneous approach of HCC.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.757 | 0.926 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".