Bibliographic record
Abstract
Abstract This chapter describes a number of factors that may influence a clinician’s judgment and conclusions while conducting an assessment, and it discusses others that make interpretation of the results less than straightforward. It begins by discussing the effects of the prevalence, or base rate, of the disorder on the diagnostic accuracy of the findings. Even in the presence of seemingly unequivocal results pointing to a given diagnosis, the findings may lead to a false-positive conclusion if the prevalence is low and to a false-negative one if the prevalence is high. The chapter shows how using Bayes’ theorem can tell us the likelihood of a wrong diagnosis. It next discusses incremental validity—whether adding another test to the battery increases diagnostic accuracy. If the new test is correlated with ones already administered, then the amount of new information it provides is limited and may increase unwarranted confidence in the final diagnosis. Third, the chapter discusses various biases and heuristics that may affect diagnostic decision-making, such as anchoring, diagnostic momentum, premature closure, and the influence of patient and assessor characteristics. It concludes by presenting a number of steps that should be taken to minimize the effects of these biases.
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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".