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Enregistrement W4200553966 · doi:10.1097/prs.0000000000008263

Reply: Identifying Factors Most Important to Lower Extremity Trauma Patients: Key Concepts from the Development of a Patient-Reported Outcome Instrument for Lower Extremity Trauma, The LIMB-Q

2021· letter· en· W4200553966 sur OpenAlexaffabout
Lily R. Mundy, Anne F. Klassen, Andrea L. Pusic, Scott T. Hollenbeck, Mark J. Gage

Notice bibliographique

RevuePlastic & Reconstructive Surgery · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueReconstructive Surgery and Microvascular Techniques
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMedicineAmputationSoft tissueSurgeryForearmThighSoft tissue injury

Résumé

récupéré en direct d'OpenAlex

We would like to thank Dr. Aytaç et al. for their comments on our recent article.1 It is evident that this is a group of clinically experienced lower extremity trauma surgeons, and we appreciate their shared interest in a patient-reported outcome instrument developed specifically for lower extremity trauma patients. The authors requested additional information on the clinical history of our qualitative interview sample, which is outlined in Table 1. The overall median time to the flap procedure was 18 days (range, 3 days to 5.5 years). Among the 22 patients with initial attempted reconstruction, 13 patients presented at the time of injury to our institution and underwent planned soft-tissue reconstruction for soft-tissue defects that were recognized at the time of injury or during early débridement. Within this group of acute reconstruction, the median time to the flap procedure was 14 days (range, 3 to 29 days). The remaining patients were transferred from an outside institution or underwent soft-tissue reconstruction for a delayed presentation of a soft-tissue defect (e.g., osteomyelitis). Free tissue transfer was performed for the majority of the soft-tissue reconstructions, with anterolateral thigh, radial forearm, and latissimus muscle flaps performed most frequently. Table 1. - Clinical Treatment Characteristics of LIMB-Q Qualitative Interview Patients* Time to Treatment/Flap Types Reconstruction (n = 15) Early Amputation(n = 11) Delayed Amputation (n = 7) Time to flap, days Mean 155.8 — 17 Median 19 — 18 Range 3–2025 — 5–27 Time to amputation, days Mean — 3 252 Median — 0 200 Range — 0–11 19–610 Free flaps, no. Anterolateral thigh 4 — 1 Radial forearm 3 — — Latissimus 4 — 1 Rectus abdominus 1 — — Local/regional flaps, no. Gastrocnemius/ soleus 2 1 — Reverse sural 1 — 2 Propeller (fasciocutaneous) — — 1 Unknown flap type 2 — 2 *Some patients had multiple flaps; some patients had bilateral injuries requiring amputation/reconstruction. Time to flap and amputation is time to first flap/amputation in patients with multiple flaps/amputations. Clinical data were not available for all patients (some were managed at an outside institution). Timing of reconstruction and flap choice can certainly have an impact on reconstructive outcomes in lower extremity trauma patients. However, the goal of our qualitative interviews was not to identify methods to optimize patient success. Rather, these interviews were conducted with the purpose of identifying all relevant concepts of interest to patients after limb-threatening lower extremity traumatic injuries. To maximize variability in the patient experience, purposeful sampling was used to ensure we were capturing a variety of experiences from patients with varying backgrounds and treatment outcomes. Interviews were conducted until we reached a point of content saturation, where no new ideas or concepts were being discussed by patients. The majority of patients received definitive treatment at a tertiary academic hospital by a select number of orthopedic trauma and reconstructive plastic surgeons. The practice patterns of these individual surgeons are likely reflected in the care of these patients. However, some patients were managed either initially or in full at outside institutions. Overall, patients in the interview sample were recruited for variability in demographic and socioeconomic backgrounds, injury etiology, and treatment outcome. The LIMB-Q is a patient-reported outcome instrument that captures the breadth of the experience for lower extremity trauma patients, which is relevant for patients who have successful outcomes and those who experience complications. We are hopeful that the qualitative patient sample reflects that variability in the patient experience to ensure that the LIMB-Q is relevant for all. We thank the authors for bringing up these points for discussion and for their interest in our shared objectives of improving the care of lower extremity trauma patients. We are confident that the LIMB-Q will help us answer many of the unanswered questions in lower extremity trauma research, while providing a stronger voice to the patient perspective. DISCLOSURE None of the authors has a financial interest to declare in relation to the content of this communication. Lily R. Mundy, M.D.Division of Plastic and Reconstructive SurgeryDepartment of SurgeryDuke UniversityDurham, N.C. Anne Klassen, D.Phil.Department of PediatricsMcMaster UniversityHamilton, Ontario, Canada Andrea L. Pusic, M.D., M.H.S.Patient Reported Outcomes, Value, and Experience Center and Division of Plastic SurgeryDepartment of SurgeryBrigham & Women’s HospitalBoston, Mass. Scott T. Hollenbeck, M.D.Division of Plastic and Reconstructive SurgeryDepartment of SurgeryDuke UniversityDurham, N.C. Mark J. Gage, M.D.Section of Orthopaedic TraumaDepartment of Orthopaedic SurgeryDuke UniversityDurham, N.C.

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)0,015
score de la tête « metaresearch » (Gemma)0,100
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,079

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,100
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0020,003
Science ouverte0,0020,002
Intégrité de la recherche0,0110,017
Charge utile insuffisante (le modèle a refusé de juger)0,0050,003

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 Opus0,054
Tête enseignante GPT0,278
Écart entre enseignants0,225 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2021
Routes d'admission2
Résumé présentoui

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