Bibliographic record
Abstract
Although most people who have headache pain do not present with an underlying mass lesion, a large number of patients with brain tumors do report headache (as many as 60% in our institution). The problem for clinicians is that the tumor-headache association is not universal, as evidenced by anecdotal reports of patients with large tumors and increased intracranial pressure, but a complete absence of headache pain. In this review, we examine more than 80 years of research on brain tumor headaches, delineating the link between tumor location, laterality, growth rate, and pain. Most importantly, we position our review within the context of current etiological theories and propose new models involving the peripheral and central sensitization of nociresponsive neurons. This review will help clinicians understand why debulking surgery sometimes fails to alleviate neoplastic headache pain in select patients. A brief examination of headaches as a result of surgery and adjuvant chemoradiation therapy is also provided. Headaches can be an early indicator of central nervous system tumors. However, headaches are present in a wide variety of other condition, and are sometimes (surprisingly) absent in patients with primary neoplasms or metastatic tumors. This observation complicates the possibility of linking headaches to brain tumors. Nevertheless, some generalizations concerning brain tumor headaches can be drawn. The following sections review these generalizations, presenting caveats where appropriate. Lingering questions in the field are also addressed and presented together with promising future research avenues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".