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Record W2512678921 · doi:10.1111/anae.13616

Frailty assessment – who, when and how?

2016· letter· en· W2512678921 on OpenAlexaboutno aff
Sarah Clayton, Kristen Barber, R. Griffiths

Bibliographic record

VenueAnaesthesia · 2016
Typeletter
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationScale (ratio)Test (biology)DownloadElective surgeryMedical emergencySurgeryWorld Wide Web

Abstract

fetched live from OpenAlex

We would like to commend Dr. O'Neill and colleagues on their recent study into the role of clinical impression in recognising frailty in the surgical population 1. We believe that the identification of frail patients will become increasingly important to anaesthetists as evidence demonstrating higher peri-operative morbidity and mortality in this population group increases, and it is reassuring that clinical impression may be a quick and reliable tool to assist in this process. The authors assessed elective surgical patients in a clinic environment, but these findings may be even more relevant to the emergency surgical population, in whom ‘eye-balling’ in the hours, or even minutes, before surgery may be the best test available. As the authors note, much of the work in this area has been limited by difficulties in both defining and measuring frailty. There are simply too many scales and indices currently being used, and as anaesthetists we require a simple, quick and reproducible tool. One such tool is the Edmonton Frail Scale, a 17-point scale validated for use by non-geriatricians to assess frailty, which takes approximately 5 min to complete per patient 2. The scale is available as an App on both Android and iOS platforms (Create Multimedia, Evergem, Belgium) and is free to download. It includes the ‘timed up and go’ test mentioned in the accompanying editorial 3, which may preclude its use in the emergency situation, but we feel it is a useful addition to the anaesthetist's armory in assessing these patients. Casting the net even wider, we may ask whether it should even fall to the anaesthetist to make these initial impressions. For elective procedures, these tools could be utilised in primary care, identifying high-risk patients who may benefit from ‘prehabilitation’ and prescribed exercise programmes to modify outcome 4, and informing postoperative destination at the point of referral. Recent evidence suggests these patients are at higher risk in the early postoperative period 5, and there may be an argument for higher dependency care in some situations. It may also be appropriate for General Practitioners to initiate and plan for appropriate enhanced social care packages following hospital discharge. It is evident that the importance of frailty as a risk factor in both emergency and elective surgery can no longer be ignored. We feel that there needs to be a greater emphasis on the identification of these patients, both in terms of anaesthetic training, and in the primary care and surgical outpatients settings. Simple tools such as the Edmonton Frail Scale may be useful when used alongside clinical impression and we would encourage readers to get out there and try this for themselves.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.285
Teacher spread0.257 · 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.

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

Citations1
Published2016
Admission routes1
Has abstractyes

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