Parents of non-verbal children with learning disability (LD) most commonly recognise their child’s pain through vocalisations, social behaviour and facial expressions
Bibliographic record
Abstract
Commentary on: Solodiuk JC. Parent described pain responses in nonverbal children with intellectual disability. Int J Nurs Stud 2013;50:1033–44.[OpenUrl][1][CrossRef][2][PubMed][3] Assessing pain in children who are non-verbal and have learning disability (LD) is challenging. Yet these children experience frequent and significant pain because of complex concomitant health conditions.1 It is widely perceived that pain responses in these children are idiosyncratic and parents report that ‘knowing’ their child is an … [1]: {openurl}?query=rft.jtitle%253DInt%2BJ%2BNurs%2BStud%26rft.volume%253D50%26rft.spage%253D1033%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.ijnurstu.2012.11.015%26rft_id%253Dinfo%253Apmid%252F23245455%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=10.1016/j.ijnurstu.2012.11.015&link_type=DOI [3]: /lookup/external-ref?access_num=23245455&link_type=MED&atom=%2Febnurs%2F17%2F4%2F111.atom
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".