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Record W4280537040 · doi:10.14245/ns.2143260.630

What Type of Incision for Anterior Cervical Spine Surgery Involving Long Segments Can Bring Better Cosmetic and Functional Outcomes?

2022· article· en· W4280537040 on OpenAlexaboutno aff
Hyung Rae Lee, Dong‐Ho Lee, Sang Yun Seok, Sehan Park, Jae Hwan Cho, Chang Ju Hwang, Choon Sung Lee

Bibliographic record

VenueNeurospine · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsnot available
FundersCervical Spine Research Society
KeywordsMedicineDysphagiaCosmesisSurgeryOtorhinolaryngology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether double transverse incisions could provide superior cosmetic and functional outcomes, including rates of dysphagia and dysphonia, compared with longitudinal incisions in patients undergoing anterior cervical spine surgery (ACSS) involving ≥ 3 levels. METHODS: A total of 62 consecutive patients who underwent ACSS involving ≥ 3 levels were included in this study. They consist of 33 with longitudinal incisions (L group) and 29 with double transverse incisions (DT group). We recorded functional outcome measures including the Bazaz score for postoperative dysphagia and the Voice Handicap Index-10 (VHI-10) for postoperative dysphonia. The Vancouver Scar Scale (VSS) and the patient and observer scar assessment scale (POSAS) were used to evaluate postoperative skin scarring. RESULTS: Cosmetic results, as assessed using the VSS and POSAS, were significantly better in the DT than in the L group at most follow-up time points (p < 0.01 each). Dysphagia rates were significantly lower in the DT group than in the L group during the late postoperative period from 6 months until final 2 years of follow-up (p < 0.01 each). There were no significant different results between the 2 groups in terms of dysphonia. CONCLUSION: A double transverse incision can be a feasible option when performing ACSS involving ≥ 3 levels, providing better cosmesis and lower rates of persistent dysphagia than with a longitudinal incision.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.126
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.299
Teacher spread0.253 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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