Adopting and Adapting Clinical Practice Guidelines for the Use of Baseline MRI in Acute Spinal Cord Injury in a Developing Country
Bibliographic record
Abstract
Background: Spinal cord injury (SCI) imposes a heavy burden on patients and health systems. Magnetic resonance imaging (MRI) provides a detailed evaluation of the spinal cord and associated soft tissues in a non-invasive manner. Objectives: We aimed to adopt and adapt suitable recommendations and guidelines in Iran for the utilization of MRI in the management of acute SCI patients based on available international guidelines and through a systematic review of literature, followed by guideline development based on the Delphi technique. Methods: After the primary systematic search and review of the literature and guidelines on the use of MRI in the management of acute SCI, all relevant recommendations were retrieved. Desired recommendations were then extracted and presented to our expert panel through the Delphi technique. The final decision for the inclusion or adaptation of recommendations to improve SCI care in the Iranian population was made through expert panel meetings. Results: Our literature search resulted in 769 records. Only three records provided recommendations on the role of MRI in the management of acute SCI, from which a total of six recommendations were extracted. Of these, the two final recommendations were extracted: (I) “Use MRI in adult patients with acute SCI prior to surgical interventions, when feasible, to facilitate clinical decision making,” and (II) “Use MRI in adult patients in the acute period following SCI and before or after surgical interventions (only when fixation is not used) to improve the prediction of neurologic outcomes following acute SCI.” Conclusions: The final recommendations help appropriately use MRI in patients with acute SCI, facilitating the management of these patients and improving their outcomes. This study shows that it is possible for developing countries to indigenize international guidelines, and with minor changes, an appropriate therapeutic framework can be created to improve service delivery.
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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 | 0.111 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".