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Record W2990273066 · doi:10.1097/brs.0000000000003276

Postoperative Care Pathways Following Lumbar Total Disc Replacement

2019· article· en· W2990273066 on OpenAlexaff
Ernest Braxton, Bryan Wohlfeld, Scott L. Blumenthal, Anthony E. Bozzio, Glenn R. Buttermann, Richard D. Guyer, Jocelyn Idema, Daniel T. Laich, Joseph Morreale, Michael A. Nikolakis, Atul T. Patel, Jack Price, Jens-Peter Witt, Jack E. Zigler, Monika Martin

Bibliographic record

VenueSpine · 2019
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsWest Fraser (Canada)
Fundersnot available
KeywordsMedicineLumbarDelphi methodRehabilitationPhysical therapySurgery

Abstract

fetched live from OpenAlex

STUDY DESIGN: A modified Delphi method was used to establish consensus. Subject matter experts were invited to participate as the expert panel. Best practice statements were distributed to the panel. Panel members were asked to mark "agree" or "disagree" after a series of statements during several rounds until either consensus could be obtained or the practice method was deemed unable to achieve consensus. OBJECTIVE: Lumbar total disc replacement (TDR) is acknowledged as an alternative to spinal fusion in appropriately selected patients. There is a lack of unanimity on the appropriate postoperative patient protocols and rehabilitation expectations for the procedure. The long-term viability of Lumbar TDR, further adoption in the community setting and specific patient outcomes are contingent on the existence of appropriate postoperative recovery programs. SUMMARY OF BACKGROUND DATA: Currently there are no established methods for postoperative care following lumbar TDR. Establishing a postoperative clinical pathway algorithm may improve patient outcomes with respect to lumbar TDR. METHOD: A lumbar TDR expert panel of 22 spine surgeons employed a modified Delphi method to drive consensus on postoperative care following single-level Lumbar TDR. The panel first reviewed literature and guidelines relevant to postoperative care following lumbar TDR. Panel members considered 21 survey questions intended to determine "standard-practice" postoperative care recommendations for patients who have undergone lumbar TDR for the initial recovery phase (0-4 wk) and rehabilitation (4-20 wk). Each panel member participated in a round of anonymous voting followed by a group discussion. Consensus was defined as 80% agreement or higher among the respondents. RESULTS: Consensus was achieved in 11 of the 21 survey questions. There was a high degree of consensus around the key goals for both the initial recovery and rehabilitation phases, ceased use of narcotics for pain management by 4 weeks postoperative, unrestricted walking immediately following surgery, timelines for physical therapy (within 2-4 wk) and return to work based on level of activity (as early as 1 wk postoperative). Lack of agreement included the use of back bracing and timing of postoperative visits. Generally, panel members felt that patient expectations regarding return to function were different following lumbar TDR versus fusion and warrant further study. CONCLUSION: Surgeon and patient alignment around postoperative expectations may significantly affect the long-term results of lumbar TDR. This surgeon consensus study found agreement for immediate postoperative ambulation, rapid reduction in opioids within the first month, and early return to work. When expectations are appropriately set with patients preoperatively, both provider and patient have shared goals in the return-to-function process. LEVEL OF EVIDENCE: 5.

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)0.037
metaresearch head score (Gemma)0.066
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 score0.037
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.066
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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