Laboratory-Related Measures of Patient Outcomes: An Introduction. Michael G. Bissell, ed. Washington, DC: AACC Press, 2000, 193 pp., $49 ($39 AACC members). ISBN 1-890883-26-3.
Bibliographic record
Abstract
If one enters the words “outcome” or “medical outcome” in a Medline search, the entries for each will number at least 44 406 for the former and 13 821 for the latter. With this volume of literature, there is an obvious need for guidance of the laboratory community for the study of outcomes. When first encountering this title, I anticipated a range of discussions focused on practical examples of the best and the worst of laboratory tests in relation to critical outcomes. The book, it turns out, is quite different from my preconceived notions. For that I am grateful and broadened. I found it illuminating and a valuable review of many concepts basic to the field of laboratory medicine. We benefit from periodically and systematically revisiting the subjects included in this review, including epidemiology, and concepts relevant …
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 | 0.044 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.002 |
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; both teacher heads agree on what is shown here.
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".