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Letters to the Editor: Evaluation and Treatment of Spinal Injuries in the Patient with Polytrauma

2004· letter· en· W2412296189 on OpenAlexaboutno aff
Eitan Melamed, Dror Robinson

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2004
Typeletter
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMethylprednisolonePolytraumaSubgroup analysisRandomizationSpinal cord injuryEuphoriantSpinal manipulationPhysical therapyRandomized controlled trialSurgerySpinal cordAlternative medicineInternal medicineLow back painPsychiatry

Abstract

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To the Editor: We would like to congratulate the authors on their detailed review of the evaluation and treatment of spinal injuries in their article titled: “Evaluation and Treatment of Spinal Injuries in the Patient with Polytrauma” by RV Patel, W DeLong Jr., and EJ Vresilovic.1 We would, however, like to make a comment concerning steroids in spinal cord injury. The authors recommend the administration of methylprednisolone in case of neurological deficit, and base their recommendation on the NASCIS II and III guidelines.2,3 Since these guidelines were published, the euphoria for this therapy was gradually replaced by the realization that the evidence available for efficacy, let alone safety, does not justify clinical usage. Much has been written regarding the unsatisfactory methods and therefore results of NASCIS, that steroids improve neurological outcome.4 The main criticism of the validity of these trails is directed towards the use of subgroup analysis. In NASCIS II, there was no benefit overall in the methylprednisolone-treated group; however, subgroup analyses detected a small gain in the total motor and sensory score in a subgroup of 62 patients who had received the drug within 8 hours of injury. The arm of the treatment group is reduced from approximately 160 patients who received methylprednisolone within a time window of 12 hours, to 62 patients in the subgroup alone. In addition to the smaller sample size, the benefits of randomization are lost when performing such analyses, yet another reason to be cautious when interpreting subgroup analysis results.5 This type of analysis carries an inherent risk of finding a difference where it actually does not exist and should be used primarily as a source of hypotheses to be tested by future research and not to change clinical policy. Additional problem with NASCIS is that it ignores the effect of surgical decompression of the spinal cord on neurological recovery. An animal study showed that surgical decompression with steroids resulted in improved neurological recovery when compared to steroids or surgery alone.6 The American Association of Neurological Surgeons/Congress of Neurological Surgeons Joint Section of Disorders of the Spine and Peripheral Nerves recently published guidelines for the management of acute cervical spine and spinal cord injuries.7 The most controversial part of the guidelines is the conclusion to recommend the use of methylprednisolone only as an option without demonstrated clinical benefit8 rather than a standard of care, due to insufficient evidence. The Canadian Neurosurgical Society published similar recommendations regarding the use of methylprednisolone in spinal cord injury,9 in which this agent is considered only as a therapeutic option. More recently, the early anecdotal evidence of adverse effects has been supported by scientific evidence that this treatment can be positively harmful, such as giving rise to acute myopathy.10 Perhaps both sides of this controversy should have been discussed. Readers are encouraged to read these guidelines and the relevant associated literature to establish their own perspective on this issue. Eitan Melamed, MD Dror Robinson, MD, PhD Department of Orthopedic Surgery, Rabin Medical Center, Petach-Tikva, Israel

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)0.004
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.016
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0040.001
Research integrity0.0160.019
Insufficient payload (model declined to judge)0.0040.004

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 head0.092
GPT teacher head0.450
Teacher spread0.359 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2004
Admission routes1
Has abstractyes

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