Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Searching the literature, a core requirement of evidence-based medicine has been impossibly oversold. The literature search is supposed to provide evidence independent from expert opinion, which has been deemed to be low on the evidence hierarchy. Yet freedom from expertise is not free. Paradoxically, practitioners are told to search the literature to avoid authority, but because there is too much information and too little time, they are urged to rely on authoritative digests. But the chain of errors inherent in searching literature for decision making, whether in scoping the decision, finding relevant documents, or in the document content, cannot be ignored. This article explores those errors. METHOD: With examples from signal theory and decision theory, the literature search is analyzed in light of fundamental limits in the nature of informaiton. You can run from expertise but you cannot hide. Expertise is inevitably required to deal with these errors. So do-it-yourself searching is inadequate in the absence of expertise. The best decisions result from collaboration with subject matter experts and decision-making experts.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.449 | 0.721 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".