Effectiveness of Surgical Treatment for Tarlov Cysts
Bibliographic record
Abstract
OF BACKGROUND DATA: In the general population, it has been estimated that 1.5% of people have ≥1 Tarlov cysts, with about 13% of those being symptomatic. Despite a range of options for treatment, there is debate about when, and how to optimally treat individuals with Tarlov cysts among clinicians, and among policy decision makers. OBJECTIVE: To summarize the current evidence on surgical treatment of Tarlov cysts. STUDY DESIGN: A systematic review was completed. METHODS: Nine databases were searched. Abstracts and full-texts were assessed by 2 reviewers. To be included, studies had to assess safety, efficacy, or effectiveness of treating Tarlov cysts, had to be written in English or French, and had to be a randomized, quasi-randomized, observational cohort, case control, or case series design including ≥2 participants. Logistic regression analysis was undertaken on the patient-level data collected to assess the association of patient and cyst characteristics on treatment success. RESULTS: In total, 31 studies were included in this systematic review; all were case series. Among the 646 participants included in these 31 studies, 210 experienced complete resolution of symptoms (32%), 327 had partial resolution (50%), 106 did not have any improvement or worsening of symptoms (16%), and 3 had their symptoms worsen after surgery (0.4%). A number of adverse events were reported after surgery; however, all were temporary. The analysis of 49 patients with data on cyst size resulted in the odds of complete resolution of symptoms being lower for patients with larger cysts (odds ratio=0.53, P-value=0.107) although this finding is not statistically significant. For those with a cyst >1.5 cm the odds of complete resolution were (odds ratio=0.36, P-value=0.190) compared with those with a cyst <1.5 cm. CONCLUSIONS: The evidence suggests that surgery for symptomatic Tarlov cysts may be an effective option for partially or completely relieving symptoms. Contrary to previous findings larger cysts were not associated with completely relieving symptoms.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".