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 to health economics included in the excellent, although brief chapter on health services. Many professional laboratorians would be well advised to use this short and readable manual to refresh the tattered edges of their knowledge about these important aspects of laboratory medicine. As such I commend it, certainly for those wishing to actually perform outcomes research, or for the considerably larger group who need to increase their understanding of outcomes research while enhancing their current familiarity with the many tools discussed in the treatise.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".