The SCOFF questionnaire was less sensitive but more specific than the ESP for detecting eating disorders
Bibliographic record
Abstract
Cotton MA, Ball C, Robinson P. Four simple questions can help screen for eating disorders. J Gen Intern Med2003 ; 18 : 53 –6 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: Are the SCOFF questionnaire and the new Eating disorder Screen for Primary care (ESP) accurate for detecting eating disorders in university students and primary care patients? Blinded comparison of the SCOFF and ESP with results from the Questionnaire for Eating Disorder Diagnosis (Q-EDD)(diagnostic standard). A large college at the University of London and a primary care clinic in London, UK. 233 participants (129 university students and 104 primary care patients). Exclusion criteria were age <18 or >65 years, inability to read English or give valid consent, or chronic illness that might result … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bgeneral%2Binternal%2Bmedicine%2B%253A%2B%2Bofficial%2Bjournal%2Bof%2Bthe%2BSociety%2Bfor%2BResearch%2Band%2BEducation%2Bin%2BPrimary%2BCare%2BInternal%2BMedicine%26rft.stitle%253DJ%2BGen%2BIntern%2BMed%26rft.aulast%253DCotton%26rft.auinit1%253DM.%2BA.%26rft.volume%253D18%26rft.issue%253D1%26rft.spage%253D53%26rft.epage%253D56%26rft.atitle%253DFour%2Bsimple%2Bquestions%2Bcan%2Bhelp%2Bscreen%2Bfor%2Beating%2Bdisorders.%26rft_id%253Dinfo%253Adoi%252F10.1046%252Fj.1525-1497.2003.20374.x%26rft_id%253Dinfo%253Apmid%252F12534764%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1046/j.1525-1497.2003.20374.x&link_type=DOI [3]: /lookup/external-ref?access_num=12534764&link_type=MED&atom=%2Febnurs%2F6%2F4%2F118.atom [4]: /lookup/external-ref?access_num=000180352600008&link_type=ISI
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".