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Record W4237693579 · doi:10.52964/amja.0403

Editorial

2015· editorial· en· W4237693579 on OpenAlexaboutno aff
Chris Roseveare

Bibliographic record

VenueAcute Medicine Journal · 2015
Typeeditorial
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarly warning scoreMedicineRespiratory rateWarning systemEmergency medicineMedical emergencyBlood pressureHeart rateInternal medicineEngineering

Abstract

fetched live from OpenAlex

The use of early warning scores to monitor the condition of patients has been one of the biggest changes in hospital practice over the past decade. This journal has featured many papers evaluating different scoring systems for medical patients admitted to hospital in an emergency; as the mechanisms for measuring, recording and calculating these scores become more refined our ability to predict which patients will deteriorate and require higher dependency care has continued to improve. In this edition, a paper from Canada has highlighted the key importance of one component of most scoring systems. Using a weighted scoring system, the authors have identified respiratory rate as the most valuable independent predictor of patient outcome. For a large cohort of patients in Thunder Bay hospital, Ontario, respiratory rate provided a true ‘early warning’ sign of imminent deterioration – rising several days before a patient’s death, and falling for patients who survived. The authors comment that respiratory rate is often inaccurately recorded – perhaps a result of the lack of an electronic measurement device or time pressures on nursing staff combined with the need to count breaths over a one minute period. This may explain why a fall in blood pressure or rise in pulse is often perceived to be more important when reviewing the observation chart at the foot of a patient’s bed. However this paper provides strong evidence to demonstrate why variations in this this clinical sign should not be overlooked. Bed pressures in UK hospitals have regularly featured in news reports over recent months. The challenge of facilitating discharge for those patients who require increased social service support after they leave hospital has had a significant impact on our emergency departments and acute medical units. However, providing a safe and effective system of triage at the hospital ‘front door’ is also key element in improving patient flow on the AMU. Acute medicine consultants are increasingly becoming involved in identifying patients whose problem can be managed without hospital admission; the evaluation of consultant-led phone triage of medical referrals to Ipswich hospital over a 12 month period indicates that this is a cost-effective solution to reduce hospital admission. The benefit was greatest for referrals from general practitioners, for whom the authors comment that sharing of the burden of risk and uncertainty is a key component of the effectiveness of the consultant-led approach. Having provided a similar service in my own hospital over the past 15 years, I would share this view; the regular phone contact also enables building of relationships between senior primary and secondary care clinicians, which is crucial if we are going to improve integration of services in the future. Finally, hospital acquired pneumonia is generally something to be avoided – but may have proved to be serendipitous for the patient in one of this edition’s case reports. During the course of his prolonged hospital stay with back pain, an MRI scan of his brachial plexus revealed incidental consolidation in his left upper zone, prompting treatment with intravenous antibiotics. Surprisingly, this treatment resulted in a reduction in his analgesic requirements; this improvement, along with the development of a lower motor neurone 7th nerve palsy led the team to investigate the possibility of Lyme neuroborreliosis, which was confirmed by serological testing. Radicular back pain and cranial neuropathies are recognised complications of Lyme disease; acute physicians reading this article should remember this when faced with this unusual combination in the future.

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)0.020
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.199
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.1990.117

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 head0.015
GPT teacher head0.339
Teacher spread0.324 · 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
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
Published2015
Admission routes1
Has abstractyes

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