Antecedents of hospital admission for deliberate self-harm from a 14-year follow-up study using data-linkage
Bibliographic record
Abstract
Antecedents of hospital admission for deliberate self-harm from a 14-year follow-up study using data-linkageFrancis Mitrou1 email, Jennifer Gaudie1 email, David Lawrence1,2 email, Sven R Silburn1,2 email, Fiona J Stanley1 email and Stephen R Zubrick1,2 email1 Telethon Institute for Child Health Research, Centre for Child Health Research, The University of Western Australia. PO Box 855, West Perth, WA. 6872, Australia2 Centre for Developmental Health, Curtin Health Innovation Research Institute, Curtin University of Technology, Perth, Western Australia, Australiaauthor email corresponding author emailBMC Psychiatry 2010, 10:82doi:10.1186/1471-244X-10-82The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-244X/10/82Received: 22 April 2010Accepted: 18 October 2010Published: 18 October 2010© 2010 Mitrou et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".