The Inflammatory Biomarkers Behavior Profile of Patients Following Elective Degenerative Spine Surgery and Differences Compared to Those Coursing With a Postoperative Spinal Infection: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: The incidence of postoperative spinal infection (PSI) ranges from 0% to 10%, with devastating effects on the patient prognosis because of higher morbidity while increasing costs to the health care system. PSIs are elusive and difficult to diagnose, especially in the early postoperative state, because of confusing clinical symptoms, rise in serum biomarkers, or imaging studies. Current research on diagnosis has focused on serum biomarkers; nevertheless, most series rely on retrospective cohorts where biomarkers are studied individually and at different time points. OBJECTIVE: This paper presents the protocol for a systematic review that aims to determine the inflammatory biomarker behavior profile of patients following elective degenerative spine surgery and their differences compared to those coursing with PSIs. METHODS: The proposed systematic review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. This protocol was registered at PROSPERO on January 19, 2022. We will include studies related to biomarkers in adult patients operated on for degenerative spinal diseases and those developing PSIs. The following information will be extracted from the papers: (1) study title; (2) study author; (3) year; (4) evidence level; (5) research type; (6) diagnosis group (elective postoperative degenerative disease or PSI); (7a) region (cervical, thoracic, lumbosacral, and coccygeal); (7b) type of infection by anatomical or radiological site; (8) surgery type (including instrumentation or not); (9) number of cases; (10) mean age or individual age; (11) individual serum biomarker values from the preoperative state up to 90 days postoperative for both groups, including (10a) interleukin-6, (10b) presepsin, (10c) erythrocyte sedimentation rate, (10d) leukocyte count, (10e) neutrophil count, (10f) C-reactive protein, (10g) serum amyloid, (10h) white cell count, (10i) albumin, (10j) prealbumin, (10k) procalcitonin, (10l) retinol-associated protein, and (10m) Dickkopf-1; (11) postoperative days at symptoms or diagnosis; (12) type of organism; (13) day of starting antibiotics; (14) duration of treatment; and (15) any biases (including comorbidities, especially those affecting immunological status). All data on biomarkers will be presented graphically over time. RESULTS: No ethical approval will be required, as this review is based on published data and does not involve interaction with human participants. The search for this systematic review commenced in February 2021, and we expect to publish the findings in mid-2023. CONCLUSIONS: This study will provide the behavior profile of biomarkers for PSI and patients following elective surgery for degenerative spinal diseases from the preoperative period up to 90 days postoperative, providing cutoff values on the day of diagnosis. This research will provide clinicians with highly trustable cutoff reference values for PSI diagnosis. Finally, we expect to provide a basis for future research on biomarkers that help diagnose more accurately and in a timely manner in the early stages of illness, ultimately impacting the patient's physical and mental health, and reducing the disease burden. TRIAL REGISTRATION: PROSPERO CRD42022304645; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304645. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41555.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".