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Record W3189848724 · doi:10.3171/2021.2.spine201879

The impact of frailty on patient-reported outcomes after elective thoracolumbar degenerative spine surgery

2021· article· en· W3189848724 on OpenAlexaffabout
Philippe Beauchamp-Chalifour, Alana M. Flexman, John Street, Charles G. Fisher, Tamir Ailon, Marcel F. Dvorak, Brian K. Kwon, Scott Paquette, Nicolas Dea, Raphaële Charest-Morin

Bibliographic record

VenueJournal of Neurosurgery Spine · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of British ColumbiaUniversité Laval
Fundersnot available
KeywordsMedicineSurgerySPINE (molecular biology)

Abstract

fetched live from OpenAlex

OBJECTIVE: Frailty has been shown to be a risk factor of perioperative adverse events (AEs) in patients undergoing various types of spine surgery. However, the relationship between frailty and patient-reported outcomes (PROs) remains unclear. The primary objective of this study was to determine the impact of frailty on PROs of patients who underwent surgery for thoracolumbar degenerative conditions. The secondary objective was to determine the associations among frailty, baseline PROs, and perioperative AEs. METHODS: This was a retrospective study of a prospective cohort of patients older than 55 years who underwent surgery between 2012 and 2018. Data and PROs (collected with EQ-5D, Physical Component Summary [PCS] and Mental Component Summary [MCS] of SF-12, Oswestry Disability Index [ODI], and numeric rating scales [NRS] for back pain and leg pain) of patients treated at a single academic center were extracted from the Canadian Spine Outcomes and Research Network registry. Frailty was calculated using the modified frailty index (mFI), and patients were classified as frail, prefrail, and nonfrail. A generalized estimating equation (GEE) regression model was used to assess the association between baseline frailty status and PRO measures at 3 and 12 months. RESULTS: In total, 293 patients with a mean ± SD age of 67 ± 7 years were included. Of these, 22% (n = 65) were frail, 59% (n = 172) were prefrail, and 19% (n = 56) were nonfrail. At baseline, the three frailty groups had similar PROs, except PCS (p = 0.003) and ODI (p = 0.02) were worse in the frail group. A greater proportion of frail patients experienced major AEs than nonfrail patients (p < 0.0001). However, despite the increased incidence of AEs, there was no association between frailty and postoperative PROs (scores on EQ-5D, PCS and MCS, ODI, and back-pain and leg-pain NRS) at 3 and 12 months (p ≥ 0.05). In general, PROs improved at 3 and 12 months (with most patients reaching the minimum clinically important difference for all PROs). CONCLUSIONS: Although frailty predicted postoperative AEs, mFI did not predict PROs of patients older than 55 years with degenerative thoracolumbar spine after spine surgery.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.159
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.324
Teacher spread0.291 · 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.

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

Citations25
Published2021
Admission routes2
Has abstractyes

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