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Record W1992527971 · doi:10.1177/1367493514527022

Surgeons’ aims and pain assessment strategies when managing paediatric post-operative pain

2014· article· en· W1992527971 on OpenAlexaffabout
Alison Twycross, Anna Williams, G. Allen Finley

Bibliographic record

VenueJournal of Child Health Care · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsMedicineContext (archaeology)Pain managementPostoperative painSet (abstract data type)Physical therapySurgery

Abstract

fetched live from OpenAlex

Children experience moderate to severe pain post-operatively. Nurses have been found to have a variety of aims in this context. Surgeons' aims when managing post-operative pain have not been explored. This qualitative study set out to explore paediatric surgeons' aims when managing post-operative pain in one paediatric hospital in Canada. Consultant surgeons (n = 8) across various specialities took part in semi-structured interviews. Surgeons' overarching aim was to keep the child comfortable. Various definitions of comfortable were given, relating to the child's experience of pain itself and their ability to undertake activities of daily living. Children's behavioural pain cues seem to be a primary consideration when making treatment decisions. Parents' views regarding their child's pain were also seen as important, suggesting children may not be seen as competent to make decisions on their own behalf. The need to maintain a realistic approach was emphasised and pain management described as a balancing act. Surgeons may draw on both tacit and explicit knowledge when assessing children's pain. There appears to be an expectation among surgeons that some pain is to be expected post-operatively and that the diagnostic value of pain may, in some cases, supersede concerns for the child's pain experience.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.311
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2014
Admission routes2
Has abstractyes

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