Bibliographic record
Abstract
Diagnostic tests should receive method- and use-effectiveness evaluations. Method-effectiveness evaluations determine sensitivity, specificity and predictive values for new tests. Use-effectiveness evaluations determine how practical or convenient a new test will be in a specific setting and may not be performed in a formal way in North American laboratories. To perform a clinical method evaluation of diagnostic tests, a good relationship between laboratory and clinical personnel is essential. Studies are usually conducted separately on populations of men and women, and should include sampling from different prevalence groups. Test performance comparisons may be made on a single specimen type or on more than one specimen from the same patient, which allows for the expansion of a reference standard and includes the ability of a particular assay, performed on a specimen type to diagnose an infected individual. The following components of the evaluation should be standardized and carefully followed: specimen identification; collection; transportation; processing; quality control; reading; proficiency testing; confirmatory testing; discordant analysis - sensitivity, specificity and predictive value calculations; and record keeping. Methods are available to determine whether sample results are true or false positives or negatives. Use-effectiveness evaluations might determine the stability or durability of supplies and equipment; the logistics of shipping, receiving and storing supplies; the clarity and completeness of test instructions; the time and effort required to process and read results; the subjectivity factors in interpretation and reporting; and the costs. These determinations are usually more apparent for commercial assays than for homemade tests.
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 | 0.050 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".