The Saudi Spine Society guidelines on spinal surgery during the COVID-19 pandemic
Bibliographic record
Abstract
In March 2020, the World Health Organization declared the novel coronavirus disease (COVID-19), which emerged in China at the end of 2019, a pandemic.By mid-April, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, had infected more than a quarter of a million people globally [1][2][3].Although the majority of those with infection experience mild illness, transmission can occur from asymptomatic individuals [3][4][5].The unpredictability and rapid spread of the disease necessitate several, critical, and immediate measures to protect healthcare workers, patients, and the community.National healthcare systems are focusing their resources to increase SARS-CoV-2 testing, to manage COVID-19 cases, and to implement preventive measures.However, during this pandemic, the need for urgent surgery will not stop.Both medical and surgical priorities have changed since the announcement of the pandemic, and they continue to evolve.Several hospitals in affected countries have taken immediate and unprecedented actions regarding patients awaiting surgery including postponing outpatient and elective surgery, canceling unnecessary operations, and suspending teaching sessions until further notice [5][6][7][8].Due to this uncertainty and individuality in the decisions, it is incumbent on medical associations and societies to establish guidelines to organize workflow and protecting patients and healthcare workers.
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 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".