Clinical research of kidney diseases II: problems of study design
Bibliographic record
Abstract
Nephrol. Dial. Transplant., 2007; doi:10.1093/ndt/gfm433 Measures of association in diagnostic studies Test (T) sensitivity (SN) and specificity (SP) are the probabilities of T+ among D+ (T+|D+) and T− among D− (T−|D−), where ‘|’ means ‘given’ or ‘conditional on’. Positive predictive value (PPV; D+|T+) and negative predictive value (NPV; D−|T−) are posterior or post-test probabilities. Sensitivity and specificity are relatively stable test characteristics since they depend on the mechanism of detection/action and the population characteristics. Conversely, PPV and NPV vary depending on disease prevalence (Pr = D+/Totals). The likelihood ratio of a positive test (LR+) is the ratio of true positive and false positive rates, SN/[1−SP]. The likelihood ratio of a negative test (LR−) is the ratio of false negative and true negative rates, (1−SN)/SP. Likelihood ratios estimate how much more likely the presence and absence of the disease are when the results of the test are positive and negative respectively. Of note, the False+ rate and False− rate correspond to the type I (alpha) and type II (beta) error rates of an outcome study. This table was previously published with errors. The authors would like to apologize for this mistake and any inconvenience.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".