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Record W2297352838 · doi:10.1111/ajag.12256

The validity of three fall risk screening tools in an acute geriatric inpatient population

2016· article· en· W2297352838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAustralasian Journal on Ageing · 2016
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConfidence intervalInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

AIM: We examined the validity of the Ontario Modified STRATIFY (OM) (St Thomas's Risk Assessment Tool in Falling Elderly Inpatients), The Northern Hospital Modified STRATIFY (TNH) and STRATIFY in predicting falls in an acute aged care unit. METHODS: Data were collected prospectively from 217 people presenting consecutively and falls identified during hospitalisation. RESULTS: Sensitivities of OM (80.0, 95% confidence interval (CI) 58.4 to 91.9%), TNH (85, CI 64.0 to 94.8%) and STRATIFY (80.0, CI 58.4 to 91.0%) were similar. The STRATIFY had higher specificity (61.4, CI 54.5 to 67.9%) than OM (37.1, CI 30.6 to 44.0%) and TNH (51.3, CI 44.3 to 58.2%). Accuracy (percentage of patients correctly classified as 'faller' or 'non-faller') was higher using STRATIFY (63.1, CI 56.5 to 69.3%) and TNH (54.4, CI 47.8 to 61.0%) than with OM (41.0, CI 34.7 to 47.7%, P < 0.0001). CONCLUSION: Screening tools have limited accuracy in identifying patients at high risk of falls.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.070
GPT teacher head0.369
Teacher spread0.299 · 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