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Record W4415761617 · doi:10.51271/icjim-0066

Frailty scoring systems in clinical practice: are we underdiagnosing?

2025· article· W4415761617 on OpenAlexaboutno aff
Mercan TAŞTEMUR

Bibliographic record

VenueIntercontinental Journal of Internal Medicine · 2025
Typearticle
Language
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsStressorGeriatricsClinical PracticePsychological resilienceMEDLINEPaceMultimorbidityResilience (materials science)Frailty syndrome

Abstract

fetched live from OpenAlex

Frailty has emerged as one of the most critical geriatric syndromes influencing morbidity, hospitalization rates, treatment tolerance, and overall survival and has emerged as one of the most critical determinants of adverse outcomes in older adults, yet it remains inconsistently identified in daily medical practice (1). Although populations are aging globally, the detection of frailty has not kept pace with clinical need. Frailty is no longer viewed as a vague geriatric concept but rather as a measurable, prognostically significant syndrome characterized by diminished physiological reserves and reduced resilience to stressors (2). Despite the availability of frailty assessment tools for nearly two decades, screening remains sporadic, and underdiagnosis persists in outpatient clinics, emergency departments, and inpatient settings (3). This disconnect between knowledge and practice has implications for mortality, hospitalization duration, functional decline, and healthcare utilization. Multiple validated instruments, such as the Fried Frailty Phenotype, Clinical Frailty Scale (CFS), Edmonton Frail Scale, and FRAIL questionnaire, have been proposed; yet, integration into internal medicine workflows is far from standardized (4). Each tool offers different levels of feasibility, sensitivity, and clinical applicability. Although these tools are validated and relatively simple to administer, they are not consistently integrated into internal medicine, cardiology, oncology, nephrology, or primary care settings. Most clinicians acknowledge frailty as a concept but do not routinely document it or formally measure it before making diagnostic or therapeutic decisions. This disconnect between awareness and implementation remains a major barrier to recognizing frailty early. Frailty is frequently overlooked due to time constraints, insufficient training, and an overemphasis on chronological age rather than biological vulnerability. In many institutions, frailty scoring is seen as optional rather than essential. As a result, patients with subtle functional decline or low physiological reserve are often categorized as “fit” or “stable” based solely on basic laboratory tests, vital signs, or self-reported independence. Without formal scoring, these individuals undergo invasive procedures, polypharmacy, or aggressive treatments without adequate risk adjustment. The absence of systematic screening prevents proactive interventions such as nutritional support, physiotherapy, medication review, or shared decision-making based on functional status (5). Failure to identify frailty promptly has direct consequences on patient outcomes. Hospitalizations become longer, complication rates rise, and recovery after acute illness or surgery is often delayed. Inappropriate medication regimens, increased risk of delirium, and higher rates of institutionalization are frequently observed in frail individuals who were never assessed formally (6). Moreover, when frailty is not documented, multidisciplinary interventions are either postponed or never initiated. Treatment goals may not align with the patient’s physiological capacity, leading to emotional distress for both patients and families. Ultimately, the health system absorbs higher costs due to preventable adverse events and prolonged care needs. Frailty scoring should not be viewed as an additional administrative step but as a clinical necessity. Integrating these tools into admission protocols, outpatient evaluations, and preoperative assessments can transform care planning. When clinicians routinely incorporate frailty scores, they are better equipped to individualize therapies, adjust drug dosages, and determine realistic rehabilitation targets. Hospitals and academic centers that embed frailty assessments into electronic health records and clinical algorithms report improved outcomes and more efficient resource allocation. Making frailty screening part of standard internal medicine practice would increase awareness, guide multidisciplinary collaboration, and improve continuity of care. Frailty is not merely a geriatric concept but a cross-disciplinary determinant of prognosis and treatment tolerance. Underdiagnosis persists because scoring systems are not routinely applied, despite being practical and valid. To close this gap, healthcare providers must adopt a proactive approach by incorporating frailty assessment into everyday clinical workflows. Education, institutional protocols, and simple screening steps can significantly improve recognition rates. By normalizing frailty scoring in internal medicine and related specialties, we can better align treatments with physiological reserve, reduce complications, and improve the quality of life for older adults.

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)0.091
metaresearch head score (Gemma)0.250
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: Empirical · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.482

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.250
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0060.006
Science and technology studies0.0020.005
Scholarly communication0.0100.011
Open science0.0080.007
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0040.002

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 head0.055
GPT teacher head0.415
Teacher spread0.360 · 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
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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