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Record W2167623644 · doi:10.1136/ebn.8.2.57

Review: capillary refill time, abnormal skin turgor, and abnormal respiratory pattern are useful signs for detecting dehydration in children

2005· letter· en· W2167623644 on OpenAlexaff
Tina Popov

Bibliographic record

VenueEvidence-Based Nursing · 2005
Typeletter
Languageen
FieldMedicine
TopicMedical Case Reports and Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsGold standard (test)MedicineDehydrationCapillary refillWeb of sciencePediatricsGastroenterologyInternal medicineChemistryMeta-analysis

Abstract

fetched live from OpenAlex

Steiner MJ, DeWalt DA, Byerley JS. Is this child dehydrated? JAMA 2004;291:2746–54.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q What is the accuracy of signs, symptoms, and laboratory tests for detecting dehydration in children? ### ![Graphic][5]</img>Data sources: Medline (1966 to April 2003), Cochrane Library , reference lists, and experts in the field. ### ![Graphic][6]</img>Study selection and assessment: studies in any language that compared signs, symptoms, and laboratory values with a recognised gold standard for diagnosing dehydration (rehydration weight minus acute weight divided by rehydration weight) in children (0–18 y). Study quality was ranked from highest (level 1 = independent, blind comparison of test with a valid gold standard) to lowest (level 5 = non-independent comparison of test with an uncertain standard of validity, which may incorporate the test result into the gold standard). ### ![Graphic][7]</img>Outcomes: sensitivity, specificity, and positive and negative likelihood … [1]: {openurl}?query=rft.jtitle%253DJAMA%26rft.stitle%253DJAMA%26rft.aulast%253DSteiner%26rft.auinit1%253DM.%2BJ.%26rft.volume%253D291%26rft.issue%253D22%26rft.spage%253D2746%26rft.epage%253D2754%26rft.atitle%253DIs%2BThis%2BChild%2BDehydrated%253F%26rft_id%253Dinfo%253Adoi%252F10.1001%252Fjama.291.22.2746%26rft_id%253Dinfo%253Apmid%252F15187057%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=10.1001/jama.291.22.2746&link_type=DOI [3]: /lookup/external-ref?access_num=15187057&link_type=MED&atom=%2Febnurs%2F8%2F2%2F57.atom [4]: /lookup/external-ref?access_num=000221862300030&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.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)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.043
GPT teacher head0.307
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2005
Admission routes1
Has abstractyes

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