Attendance at accident and emergency for deliberate self harm predicts increased risk of suicide, especially in women
Bibliographic record
Abstract
Cooper J, Kapur N, Webb R, et al . Suicide after deliberate self-harm: a 4-year cohort study. Am J Psychiatry 2005;162:297–303.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q How prevalent is suicide in people who have committed deliberate self harm? ### ![Graphic][5]</img>Design: Prospective cohort study. ### ![Graphic][6]</img>Setting: Four accident and emergency departments in Manchester, UK; 1997 to 2001. ### ![Graphic][7]</img>Population: 7968 people (median age of 30 years) attending accident and emergency because of deliberate self harm between September 1997 and August 2001. ### ![Graphic][8]</img>Prognostic factors: Attendance at accident and emergency for deliberate self harm. ### ![Graphic][9]</img>Outcome: Suicide rates. Deaths by suicide were identified using the National Confidential Inquiry Into Suicide and Homicide by People With Mental Illness database of the Office of National Statistics. Confirmed suicides and deaths from unknown cause (ICD-9 codes) were considered suicides. Suicide rates in the study population were compared with those for general population of Manchester to give standardised mortality ratios (SMRs). ### ![Graphic][10]</img>Follow up period: Four years. Between September 1997 and August 2001, the suicide rate … [1]: {openurl}?query=rft.jtitle%253DAmerican%2BJournal%2Bof%2BPsychiatry%26rft.stitle%253DAm.%2BJ.%2BPsychiatry%26rft.aulast%253DCooper%26rft.auinit1%253DJ.%26rft.volume%253D162%26rft.issue%253D2%26rft.spage%253D297%26rft.epage%253D303%26rft.atitle%253DSuicide%2BAfter%2BDeliberate%2BSelf-Harm%253A%2BA%2B4-Year%2BCohort%2BStudy%26rft_id%253Dinfo%253Adoi%252F10.1176%252Fappi.ajp.162.2.297%26rft_id%253Dinfo%253Apmid%252F15677594%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=10.1176/appi.ajp.162.2.297&link_type=DOI [3]: /lookup/external-ref?access_num=15677594&link_type=MED&atom=%2Febmental%2F8%2F4%2F97.atom [4]: /lookup/external-ref?access_num=000227210800015&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".