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Record W4376108910 · doi:10.1097/brs.0000000000004714

Ninety Percent of Patients Are Satisfied With Their Decision to Undergo Spine Surgery for Degenerative Conditions

2023· article· en· W4376108910 on OpenAlexaboutno aff
Omri Maayan, Pratyush Shahi, Robert Merrill, Anthony Pajak, Amy Lu, Yousi A. Oquendo, Tejas Subramanian, Kasra Araghi, Olivia Tuma, Maximilian Korsun, Tomoyuki Asada, Nishtha Singh, Sumedha Singh, Evan Sheha, James Dowdell, Sheeraz A. Qureshi, Sravisht Iyer

Bibliographic record

VenueSpine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesWeill Cornell Medical CollegeHospital for Special Surgery
KeywordsMedicineSPINE (molecular biology)Degenerative diseaseSurgeryPhysical therapyCentral nervous system diseaseBioinformatics

Abstract

fetched live from OpenAlex

STUDY DESIGN: Cross-sectional survey and retrospective review of prospectively collected data. OBJECTIVE: To explore how patients perceive their decision to pursue spine surgery for degenerative conditions and evaluate factors correlated with decisional regret. SUMMARY OF BACKGROUND DATA: Prior research shows that one-in-five older adults regret their decision to undergo spinal deformity surgery. However, no studies have investigated decisional regret in patients with degenerative conditions. METHODS: Patients who underwent cervical or lumbar spine surgery for degenerative conditions (decompression, fusion, or disk replacement) between April 2017 and December 2020 were included. The Ottawa Decisional Regret Questionnaire was implemented to assess prevalence of decisional regret. Questionnaire scores were used to categorize patients into low (<40) or medium/high (≥40) decisional regret cohorts. Patient-reported outcome measures (PROMs) included the Oswestry Disability Index, Patient-reported Outcomes Measurement Information System, Visual Analog Scale (VAS) Back/Leg/Arm, and Neck Disability Index at preoperative, early postoperative (<6 mo), and late postoperative (≥6 mo) timepoints. Differences in demographics, operative variables, and PROMs between low and medium/high decisional regret groups were evaluated. RESULTS: A total of 295 patients were included (mean follow-up: 18.2 mo). Overall, 92% of patients agreed that having surgery was the right decision, and 90% would make the same decision again. In contrast, 6% of patients regretted the decision to undergo surgery, and 7% noted that surgery caused them harm. In-hospital complications (P=0.02) and revision fusion (P=0.026) were significantly associated with higher regret. The medium/high decisional regret group also exhibited significantly worse PROMs at long-term follow-up for all metrics except VAS-Arm, and worse achievement of minimum clinically important difference for Oswestry Disability Index (P=0.007), Patient-Reported Outcomes Measurement Information System (P<0.0001), and VAS-Leg (P<0.0001). CONCLUSIONS: Higher decisional regret was encountered in the setting of need for revision fusion, increased in-hospital complications, and worse PROMs. However, 90% of patients overall were satisfied with their decision to undergo spine surgery for degenerative conditions. Current tools for assessing patient improvement postoperatively may not adequately capture the psychosocial values and patient expectations implicated in decisional regret.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.209
GPT teacher head0.426
Teacher spread0.217 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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