Bibliographic record
Abstract
The principlePain is transmitted by afferent nerves from skin, organs, muscle, and other related structures in the lower abdomen and pelvis, while muscle spasm can be maintained by efferent nerves.These nerves are modulated centrally in the nervous system and can become upregulated with subsequent sensitization of the end organ.By injecting the nerves with local anesthetic, we effectively block this process.If we are accurate, the pain resolves, even if temporarily.This fact in itself allows for much better diagnosis of the cause of the patient's pain.However, a phenomenon that can occur by temporarily breaking this pain cycle is that when the pain returns, it may not be as severe as it was before the injection.With repeated injections, we may be able to downregulate or desensitize the chronic pain cycle.While this is a very gross simplification of what we are trying to accomplish, it is the explanation that I provide to all my patients.And they understand the rationale and are willing to give it a try.For practical purposes, my first injection therapy will be a test, at trial to see if the pain resolves.For this first test, I will use half lidocaine 2% and half bupivacaine (0.25%).If that is successful in ameliorating the pain to the patient's satisfaction, I will offer repeat injections (weekly if possible, but sometimes biweekly) with 0.25% bupivacaine.Table 1 outlines my maximum doses per injection session.I only use a steroid (cortisone or triamcionolone) if I believe that I am dealing with an inflammatory condition and/or nerve entrapment syndrome.Once we start to see success, I space out the injections to monthly and then only as required by the patient.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".