Bibliographic record
Abstract
The process results of the 8 December 2025 searches of Google, OVID, PubMed, Scopus, and Web of Science for the keywords “tumor” and “time to discovery”. The initial number of records identified was 168: Google Scholar (n = 13), OVID (n = 8), PubMed (n = 32), Scopus (n = 62), and Web of Science (n = 53). Supplementary File S1 records the process. The following is the breakdown of the exclusionary process, box-by-box. Following the PRISMA-ScR guidelines, after the “Records identified”, the “Records removed before screening” does not differentiate by the databases. Supplementary File S1 provides the individual identification. The Duplicate records removed in total were (n = 10). By databases, they were OVID—duplicated with itself—(n = 3), Scopus—duplicated with PubMed—(n = 2), and Web of Science—duplicated with Scopus—(n = 5). Those that were not an empirical study were (n = 6). They are from Google Scholar (n = 3), OVID (n = 1), and Web of Science (n = 2). Those not peer-reviewed were from Google Scholar (n = 3), leaving the “Records screened” (n = 149). The excluded records were then those studies of non-human subjects (n = 18). By database: PubMed (n = 2), Scopus (n = 4), and Web of Science (n = 12), leaving the “Reports sought for retrieval” (n = 131). The “Reports not retrieved” were (n = 8). Google had (n = 1) unretrievable, PubMed (n = 5), and Scopus (n = 2), resulting in the “Reports assessed for eligibility” (n = 123). The “Reports excluded” regarded no tumor or no time to discovery. “No tumor” was (n =10). The breakdown by database was Google Scholar (n = 2), PubMed (n = 7), and Scopus (n = 1). Those lacking time to discovery were (n = 87). By database, they were OVID (n = 2), PubMed (n = 18), Scopus (n = 45), and Web of Science (n = 22), leaving the “Studies included in review” (n = 26). Of these, Google Scholar represents (n = 4), OVID (n = 2), PubMed (n = 0), Scopus (n = 8), and Web of Science (n = 12). Of the (n = 26) “Studies included in review”, there were also (n = 26) “Reports of included studies”. Although the search of each database was for the phrase “time to discovery”, some of the included studies lacked that exact wording. Nevertheless, upon examining the content of the article, the determination was that there was an equivalent phrase to “time to discover”. Two OVID results used the time of discovery. For Scopus results, similar phrases were judged to be the following: (1) time to transformation, (2) time to affect, (3) time to biochemical recurrence, (4) time from sample date, (5) time of a given scan, (6) time interval from baseline, (7) time of diagnosis, and (8) time from diagnosis to death or relapse. For Web of Science, the phrases were as follows: (1) time of recurrence, (2) time of confirmed tumor diagnosis, (3) time between diagnosis and surgery, (4) time of diagnosis, (5) time of evaluation, (6) time points of tumor progression, (7) time-to-recurrence, (8) time of initial diagnosis, (9) time from randomization, (10) time of collection. Unlike the other searches, the relevant phrase in the Google Scholar search was time to discovery. With no included results, the phrases in PubMed are irrelevant for this review.
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 | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.291 | 0.139 |
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".