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Enregistrement W4415761617 · doi:10.51271/icjim-0066

Frailty scoring systems in clinical practice: are we underdiagnosing?

2025· article· W4415761617 sur OpenAlexaboutno aff
Mercan TAŞTEMUR

Notice bibliographique

RevueIntercontinental Journal of Internal Medicine · 2025
Typearticle
Langue
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStressorGeriatricsClinical PracticePsychological resilienceMEDLINEPaceMultimorbidityResilience (materials science)Frailty syndrome

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,091
score de la tête « metaresearch » (Gemma)0,250
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,091
Score d'incertitude au seuil0,482

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0910,250
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0060,006
Études des sciences et des technologies0,0020,005
Communication savante0,0100,011
Science ouverte0,0080,007
Intégrité de la recherche0,0060,010
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,055
Tête enseignante GPT0,415
Écart entre enseignants0,360 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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