The diagnostic value of Red Flags in thoracolumbar pain: a systematic review
Bibliographic record
Abstract
Red Flags (RFs) are signs and symptoms related to the screening of serious underlying pathologies mimicking a musculoskeletal pain. The current literature wonders about the usefulness of RFs, due to high false-positive rates and low diagnostic accuracy. The aims of this systematic review are: (a) to identify and (b) to evaluate the most important RFs that could be found by a health care professional during the assessment of patients with low and upper back pain (named as thoracolumbar pain (TLP)) to screen serious pathologies. A systematic review of the literature was conducted. Searches were performed on seven databases (Pubmed, Web of Science, Cochrane Library, Pedro, Scielo, CINAHL, and Google Scholar) between March 2019 and June 2020, using a search string which included synonyms of low back pain (LBP), chest pain (CP), differential diagnosis, RF, and serious disease. Only observational studies enrolling patients with LBP or CP were included. Risk of bias was assessed with the Newcastle Ottawa Scale and inter-rater agreement between authors for full-text selection was evaluated with Cohen’s Kappa. Where possible the diagnostic accuracy was recorded for sensitivity (Sn), specificity (Sp), and positive/negative likelihood ratio (LR+/LR–). Forty full-texts were included. Most of the included observational studies were judged as low risk of bias, and Cohen’s Kappa was good (=0.78). The identified RFs were: advanced age; neurological signs; history of trauma; malignancy; female gender; corticosteroids use; night pain; unintentional weight loss; bladder or bowel dysfunction; loss of anal sphincter tone; saddle anaesthesia; constant pain; recent infection; family or personal history of heart or pulmonary diseases; dyspnoea; fever; postprandial CP; typical reflux symptoms; haemoptysis; sweating; pain radiated to upper limbs; hypotension; retrosternal pain; exertional pain; diaphoresis; and tachycardia. The diagnostic accuracy of RFs as self-contained screening tool was low, while the combination of multiple RFs showed to increase the probability to identify serious pathologies. Despite the use of single RF should not be recommended for the screening process in clinical practice, the combination of multiple RFs to enhance diagnostic accuracy is promising. Moreover, the identified RFs could be a baseline to develop a screening tool for patients with TLP.Implications for rehabilitationDifferential diagnosis and screening for referral are mandatory skills for each healthcare professional in direct access clinical settings, and should be the primary step for an appropriate management of a patient with signs and symptoms mimicking serious pathologies in thoracolumbar region.Clinical reasoning and decision-making processes are essential throughout all phases of a patient’s pathway of care. By which, the use of single Red Flag (RF) as a self-contained screening tool should not be recommended. The combination of multiple RFs promises to increase diagnostic accuracy and could grow into an excellent screening tool for thoracolumbar pain. Differential diagnosis and screening for referral are mandatory skills for each healthcare professional in direct access clinical settings, and should be the primary step for an appropriate management of a patient with signs and symptoms mimicking serious pathologies in thoracolumbar region. Clinical reasoning and decision-making processes are essential throughout all phases of a patient’s pathway of care. By which, the use of single Red Flag (RF) as a self-contained screening tool should not be recommended. The combination of multiple RFs promises to increase diagnostic accuracy and could grow into an excellent screening tool for thoracolumbar pain.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".