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Record W2091902774 · doi:10.1136/ebn.7.3.73

Nurse led education plus direct access to imaging improved diagnosis and management of urinary tract infections in children

2004· letter· en· W2091902774 on OpenAlexaff
Cynthia Kitson

Bibliographic record

VenueEvidence-Based Nursing · 2004
Typeletter
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsQueensway-Carleton Hospital
Fundersnot available
KeywordsIMGMedicinePediatricsUrinary systemIncidence (geometry)General practiceGynecologyInternal medicineFamily medicineMathematics

Abstract

fetched live from OpenAlex

Coulthard MG, Vernon SJ, Lambert HJ, et al . A nurse led education and direct access service for the management of urinary tract infections in children: prospective controlled trial. BMJ 2003;327:656–9.[OpenUrl][1][Abstract/FREE Full Text][2] Q In general practice, does nurse practitioner (NP) led education plus direct access to imaging improve diagnosis and management of urinary tract infections (UTIs) in children? ### ![Graphic][3] Design: cluster randomised controlled trial. ### ![Graphic][4] Allocation: {concealed}*. ### ![Graphic][5] Blinding: unblinded. ### ![Graphic][6] Follow up period: mean 20 months. ### ![Graphic][7] Setting: 88 general practices in a paediatric nephrology secondary catchment area in the UK. ### ![Graphic][8] Participants: 107 100 children who were followed up for incidence of UTIs. ### ![Graphic][9] Intervention: 44 general practices were allocated to a NP led intervention (NLI) (n = 55 800 children and 185 physicians) and 44 to usual care (UC) (n = 51 300 children and 161 physicians). Physicians in the NLI group were educated about the study and new management guidelines. Physicians used the … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.aulast%253DCoulthard%26rft.auinit1%253DM.%2BG%26rft.volume%253D327%26rft.issue%253D7416%26rft.spage%253D656%26rft.atitle%253DA%2Bnurse%2Bled%2Beducation%2Band%2Bdirect%2Baccess%2Bservice%2Bfor%2Bthe%2Bmanagement%2Bof%2Burinary%2Btract%2Binfections%2Bin%2Bchildren%253A%2Bprospective%2Bcontrolled%2Btrial%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.327.7416.656%26rft_id%253Dinfo%253Apmid%252F14500439%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/ijlink?linkType=ABST&journalCode=bmj&resid=327/7416/656&atom=%2Febnurs%2F7%2F3%2F73.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif [6]: /embed/inline-graphic-4.gif [7]: /embed/inline-graphic-5.gif [8]: /embed/inline-graphic-6.gif [9]: /embed/inline-graphic-7.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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.333
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2004
Admission routes1
Has abstractyes

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