The Suicide-Risk Factor – Data Query Tool (SURF-DQT): easy and handy access to the exhaustive base of highest evidence suicide risk factors
Bibliographic record
Abstract
The extensive number of publications on suicide risk factors presents a practical challenge for clinicians, policy makers, and researchers to translate the research findings from academia to the improvement of individual suicide predictions and collective prevention. The Suicide Risk-Factor - Data Query Tool (SURF-DTQ) is a web application that collates organized data and helps with meta-analysis of suicidality data. Through a process of systematic review of literature according to PRISMA standards, screening and extracting studies with designs of a high level of evidence, and multi-level data synthesis, information is presented in the SURF database in a summarized, structured, and immediately relevant form. SURF allows users to search by risk factor, population, date, and indicator filters, enabling users to quickly access the most up to date and empirically grounded general or specific knowledge about suicide-related risk factors.
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 | 0.022 | 0.136 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.040 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.029 |
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".