Best Practices for Biofuel Policy: What Canada's Biofuel Industry Can Learn From Experiences in the U.S., the E.U., and Brazil
Bibliographic record
Abstract
In studies of diagnostic test accuracy, authors sometimes report results only for a range of cutoff points around data-driven "optimal" cutoffs. We assessed selective cutoff reporting in studies of the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool. We compared conventional meta-analysis of published results only with individual-patient-data meta-analysis of results derived from all cutoff points, using data from 13 of 16 studies published during 2004-2009 that were included in a published conventional meta-analysis. For the "standard" PHQ-9 cutoff of 10, accuracy results had been published by 11 of the studies. [...] When the PHQ-9 was highly sensitive, authors more often reported results for higher cutoffs. Consequently, in the conventional meta-analysis, sensitivity increased as cutoff severity increased across part of the cutoff range-an impossibility if all data are analyzed. In sum, selective reporting by primary study authors of only results from cutoffs that perform well in their study can bias accuracy estimates in meta-analyses of published results.
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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 | 0.039 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".