Is there an optimum number needed to retrieve to justify inclusion of a database in a systematic review search?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To determine whether calculation of a 'Number Needed to Retrieve' (NNTR) is possible and desirable as a means of evaluating the utility of a database for systematic review. METHODS: To determine an overall NNTR, eight systematic reviews were tracked to determine how many abstracts were retrieved compared to the number of articles meeting the inclusion criteria. An NNTR was calculated for each database searched to measure the utility of including it in systematic review searches. RESULTS: Across eight systematic reviews, 17 378 abstracts were reviewed. Of these, 122 met the inclusion criteria for their reviews resulting in an overall NNTR of 142. Individual reviews had an NNTR range of 28-310. Three databases delivered unique results (medline, cinahl and globalhealth). The majority of the included studies appeared in multiple databases. Only five articles were found in a single database. CONCLUSIONS: This research offers a proof of concept of 'NNTR'. While the eight review NNTRs varied widely, all were consistent with the range initially reported by Booth. Included articles consistently appeared in multiple databases, suggesting that duplicate abstracts should be screened first as these are likely to include highly relevant, high-quality results.
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.
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 | 0.187 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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 it